Logic Sensing of MicroRNA in Living Cells Using DNA-Programmed

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Logic Sensing of MicroRNA in Living Cells Using DNAProgrammed Nanoparticle Network with High Signal Gain Renye Yue, Zhi Li, Ganglin Wang, Junying Li, and Nan Ma ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.8b01422 • Publication Date (Web): 06 Dec 2018 Downloaded from http://pubs.acs.org on December 8, 2018

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Logic Sensing of MicroRNA in Living Cells Using DNA-Programmed Nanoparticle Network with High Signal Gain Renye Yue†, Zhi Li†, Ganglin Wang†, Junying Li‡, Nan Ma*† † The Key Lab of Health Chemistry and Molecular Diagnosis of Suzhou, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, 215123, P. R. China (E-mail: [email protected]) ‡ Bio-Ultrastructure Analysis Lab, Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University

Abstract Molecular circuits capable of implementing Boolean logic in cellular environments have emerged as an important tool for in situ sensing, elucidating, and modulating cell functions. The performance of existing molecular computation devices in living cells is limited because of the low level of biomolecular inputs and moderate signal gain. Herein, we devised a new class of DNA-programmed nanoparticle network with integrated molecular computation and signal amplification functions for logic sensing of dual microRNA (miRNA) molecules in living cells. The nanoparticle network, which is composed of DNA-bridged gold nanoparticles and quantum dots (QDs), could simultaneously interface with two miRNA molecules, amplify the molecular inputs, perform a calculation through AND logic gate, and generate QD photoluminescence (PL) as an output signal. Significant improvement in imaging sensitivity is achieved by integrating the signal amplifier into the molecular computation device. It allows discrimination of specific cancer cell type via intelligent sensing of miRNA patterns in living cells. Keywords: microRNA, logic gate, quantum dot, gold nanoparticle, DNA, assembly, cancer imaging Molecular computation in living cells holds great promise for intelligent diagnostics, monitoring complex biological processes, and predicting cellular functions.1,2 DNA computation has offered unprecedented opportunities for logic sensing of a range of biomolecules in vitro,3-5 whereby a conclusion could be reached autonomously with high efficiency and accuracy. Despite these successes,

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development of molecular computation sensors in living cells is still in its infancy. While DNA-based nanodevice have been constructed to implement Boolean logic in living cells,6,7 the performance in signal gain remains a bottleneck for sensing applications as the input molecules only trigger an equivalent amount of output molecules through logic operations. Electronic circuits make use of signal amplifiers to increase the amplitude of signals. Likewise, molecular signal amplifiers could be integrated into molecular circuits to achieve high signal gain.8,9 Nevertheless, high-performance intelligent sensors with integrated molecular computation and signal amplification functions for sensing of low-abundance nucleic acids molecules in living cells still remain underdeveloped. MicroRNA (miRNA) is a family of small endogenous non-coding RNA molecules that play crucial roles in regulation of gene expression and cell functions.10-12 Abnormal levels of microRNA expression in mammalian cells are implicated in many types of diseases,13,14 highlighting their potential as biomarkers for disease diagnostics.15-22 It has been revealed that the miRNA expression patterns are cell type-specific,23 thereby offering high predication accuracy in regard to cancer type and stages of cancer development in comparison with single miRNA analysis.24 Accurate and autonomous detection of specific cancer cell types necessitates an intelligent sensor that could simultaneously interface with different miRNA molecules, perform a calculation via built-in Boolean logic, and generate a result by transducing the miRNA signature to light output. However, the low-abundance of miRNA in living cells substantially limits the output signal of the molecular circuit for accurate and sensitive in situ analysis. QD have proven to be excellent luminophores for biosensing and bioimaging applications because of their strong photoluminescence (PL) and high photostability.25-29 Biofunctionalized QDs have been utilized for targeting and imaging of cell surface or intracellular biomarkers in living cells.3036

We have previously developed a class of DNA-templated GNP-QDs satellite nanostructure for high-

sensitive detection of single miRNA target in living cancer cells, which leverages the high brightness of QDs and a DNA-programmed signal amplification capability.22,37 However, this approach is based on single miRNA analysis and therefore is incapable of identifying specific cancer cell types. So far QDsbased molecular computation probe for logic sensing of dual miRNA pattern in living cells to distinguish different cancer cell types has not been demonstrated. Results and discussion


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Herein, we report a new class of DNA-programmed GNP-QD network (GQN) with both molecular computation and signal amplification capacities for intelligent sensing of dual miRNA molecules (miRNA-21 and miRNA-122) in living cancer cells. MiRNA-21 is ubiquitously overexpressed in various cancer cell types whereas miRNA-122 is specifically overexpressed in liver cells.38,39 The GQN sensor features two parallel signal amplifiers coupled to an AND gate, whereby the molecular information of two miRNA molecules are amplified, computed, and transduced into QD PL as an output (Scheme 1a). GQN is constructed by assembling heterobivalent DNA-functionalized QD (DNA3-QD) with DNA1-GNP and DNA2-GNP (A1 and A2) through each arm using two linker DNA molecules (L-1 and L-2) (Scheme 1b and 1c). The QD PL remains quenched by the adjacent GNPs via fluorescence resonance energy transfer (FRET). MiRNA-21 could specifically catalyze disassembly of A1 and QD, and miRNA-122 could specifically catalyze disassembly of A2 and QD. The catalytic disassembly, which functions as a signal amplifier, is based on entropy-driven two-step DNA strand displacement reactions (SDR) with the aid of fuel DNA (Fuel-1 and Fuel-2).40 Accordingly, The GQN serves as an AND gate by generating a HIGH QD PL output (1) only if both of the two inputs (miRNA-21 and miRNA-122) are HIGH (1, 1). An input of (1, 0) or (0, 1) (miRNA-21 or miRNA-122 alone) leads to partial disassembly of GQN, which yields A2-QD and A1-QD conjugates respectively with quenched QD PL as LOW output (0) (Scheme 1a). DNA-functionalized GNPs (A1 and A2) and heterobivalent QDs (DNA3-QD) were prepared following previously reported protocols.33,41 The as-prepared GNPs have a mean diameter of 20.8 nm and an absorption peak at 520 nm (SI Figure S1). DNA3-QDs possess an absorption peak at 573 nm and an emission peak at 618 nm (SI Figure S2a). The mean diameter of these QDs is 3.4 nm as shown in transmission electron microscopy (TEM) images (SI Figure S2b). The heterobivalency of the QDs was verified by native polyacrylamide gel electrophoresis and gel filtration chromatography (SI Figure S3). A1 and A2 were assembled with L-1 and L-2 respectively via hybridization and purified. The average numbers of L-1 and L-2 on A1 and A2 were determined to be 20 and 8 using a DTT displacement assay. To prepare GQN, the above A1 and A2 assembled with L-1 and L-2 were combined at certain molar ratio and then mixed with DNA3-QD. Assembly was conducted at 37 °C for 3 hours and the final product was purified via centrifugation. The assembly process was monitored by agarose gel electrophoresis. Assembly of DNA3-QD with A1 or A2 alone leads to discrete nanostructures with retarded mobility in the gel. Assembly of DNA3-QD with both A1 and A2 leads to


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larger structures trapped in the well (SI Figure S4). TEM images show that the final product contains multiple closely associated GNPs forming a network with a dimension of roughly 50-150 nm (Figure 1a). The average number of GNPs in each GQN was determined to be 7.3 according to statistics data (SI Figure S5). Numerous QDs with weak contrast in between the GNPs could be identified in the high-resolution TEM image (Figure 1b). Energy-dispersive X-ray spectroscopy (EDX) confirms the presence of Au and Cd elements in GQN (Figure 1c). The average hydrodynamic size of GQN is 126 nm as measured by dynamic light scattering (DLS) (Figure 1d). Production of GQN is dependent on the amount of DNA3-QD introduced for the assembly. The optimal molar ratio of A1/A2/QD for GQN synthesis is determined to be 3:5:100 (SI Figure S6). Next, we evaluated the molecular computation capacity of GQN by monitoring the catalytic disassembly process with single and double inputs. Two DNA catalysts (C’-1 and C’-2) that mimic miRNA-21 and miRNA-122 respectively were used as inputs to trigger the disassembly. As shown in Figure 2a, C’-1 (0.05×) induces the disassembly of A1 and QD in the presence of F-1, resulting in A1 and A2-QD that correspond to the lower band and upper band respectively in the agarose gel. It is noteworthy that the A2-QD band is slightly darker than A1 because the molar ratio of A1:A2 is 3:5 in the GQN. Similarly, C’-2 (0.05×) induces the disassembly of A2 and QD in the presence of F-2 to generate A2 and A1-QDs. Introduction of both C’-1 (0.05×) and C’-2 (0.05×) induces simultaneous disassembly of A1, A2 and QD, resulting in a single band corresponds to mixture of A1 and A2 in the gel. The catalytic disassembly did not proceed in the absence of F-1 and F-2 (SI Figure S7). Meanwhile, PL of released QDs under each condition was recorded. As shown in Figure 2b, significant enhancement of QD PL intensity (8055) is detected with double inputs of C’-1 and C’-2. In contrast, single input of C’-1 or C’-2 alone only induces weak enhancement of QD PL intensity (1146 and 1268), which could be attributed to the release of a small portion of “single-bonded” QDs in GQN. A QD PL threshold (3000) is assigned to define the HIGH (1) and LOW (0) outputs for AND logic gate (Figure 2c). GQN prepared under other A1:A2 molar ratios (2:5 and 4:5) leads to unequal and higher signal background induced by single inputs (SI Figure S6). The catalytic disassembly could be accelerated when using a longer toehold (7 nt) for fuel DNA strand (SI Figure S8). The sensitivity of GQN was investigated through titration experiments with different concentrations of catalyst DNA. Gradual increase of QD PL was observed for the GQN treated with increasing


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concentration of catalyst DNA C’-1 and C’-2 in the presence of fuel DNA (the molar ratio of C’-1/L-1 and C’-2/L-2 were kept same) (Figure 3a). The GQN is most responsive in the low target concentration range at C’/L molar ratio from 0 to 0.05, where a near-linear correlation between target concentration and QD PL intensity is obtained (Figure 3b). To validate the catalytic disassembly process, the titration experiments were conducted in the absence of fuel DNA that is required to maintain the catalytic turnover. In that case QD PL was detected only at high C’/L molar ratio (1:1) as a result of noncatalytic single SDR (Figure 3c). These results confirm the signal amplification capacity of GQN for dual targets sensing. Next, we profiled the detection sensitivity of GQN for each target by keeping the concentration of one target constant (molar ratio of C’-1/L-1 = 1 or C’-2/L-2 = 1) and varying the concentration of the other target (molar ratio of C’-2/L-2 or C’-1/L-1 from 0 to 1). As shown in Figure 4, the GQN exhibits high responsivity to each target within low target concentration ranges. A nearlinear correlation between target concentration and QD PL intensity was achieved at C’-1/L-1 or C’2/L-2 molar ratio from 0 to 0.025. The limit of detection (LOD) for C’-1 and C’-2 was determined to be 46.0 and 39.6 pM respectively. Additionally, titration experiments against fuel DNA show that F1/L-1 or F-2/L-2 molar ratio of 1 is sufficient to induce complete disassembly of GQN in the presence of C’-1 or C’-2 (0.05×) (SI Figure S9). The calibration curves for single target detection were further applied to quantify miRNA-21 and miRNA-122 isolated from living cells. Total RNAs in three cell lines – HEK-293 (normal), MCF-7 (breast cancer), and Huh-7 (hepatocellular carcinoma) were extracted and treated with GQN, and the QD PL was recorded for each sample. The miRNA-21 and miRNA-122 levels in HEK-293, MCF-7, and Huh-7 cells were determined to be 562 and 384, 7037 and 826, 8192 and 14972 copies/cell respectively (Figure 4e). Accordingly, we assigned a threshold of miRNA expression levels (2000 copies/cell) in living cells to define the HIGH (1) and LOW (0) inputs. The miRNA expression levels determined using this method are consistent with that of quantitative polymerase chain reaction (qPCR) method (SI Figure S10 and Table S1). We subsequently utilized GQN as an intracellular molecular computation sensor for miRNA-based discrimination and imaging of specific cancer cell types. The GQN exhibits high stability against nuclease DNase I digestion (50 U/L) (SI Figure S11) as well as minimal cytotoxicity to various cell lines (SI Figure S12). Lipofectamine-2000 was used as vehicle for efficient co-delivery of GQN sensor and fuel DNA into living cells. HEK-293, MCF-7, and Huh-7 cells with distinct miRNA-21 and miRNA-122 patterns were used for the study. TEM images of the cells show that the GQN entered the


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cells and remained intact after 1 hour incubation (Figure 5). After 6 hours, the GNPs were disassembled in MCF-7 and Huh-7 cells but not in HEK-293 cells (Figure 5), indicating that the disassembly process is specific to cell types. The cells were stained with 4',6-diamidino-2-phenylindole (DAPI) for fluorescence imaging studies. Cells transfected with GQN in the absence of fuel DNA were used as controls in order to test the signal amplification performance. As shown in Figure 6, the GQN sensor could specifically illuminate Huh-7 cells containing high expression of both miRNA-21 and miRNA-122. In contrast, negligible QD PL was detected in HEK-293 cells with minimal level of miRNA-21 and miRNA-122. Also, only slight QD PL was detected in MCF-7 cells with high miRNA21 expression but minimal miRNA-122 expression. These results suggest successful implementation of AND logic gate in living cells. In MCF-7 cells, the GQN was partially disassembled into A1 and A2QD by miRNA-21 alone. Although the GNPs were disassembled as observed in the TEM image, the QDs (invisible in TEM image) remained attached to A2 GNPs which leads to quenching of QD PL. In Huh-7 cells, the GQN was completely disassembled into A1, A2, and QD by miRNA-21 and miRNA122. In that case the QD PL was no longer quenched by the GNPs. Moreover, the signal amplifier integrated in GQN is necessary to afford high signal gain in living cells. The GQN sensor without signal amplification function did not generate detectable QD PL for all the three cell types (Figure 6). For cell imaging studies, a threshold value of mean PL intensity (5) is assigned to define the HIGH (1) and LOW (0) outputs. Conclusion Taken together, we developed a new class of DNA-programmed nanoparticle network for logic sensing of miRNA molecules in living cells with high signal gain. This intelligent sensor allows accurate discrimination and high-sensitive imaging of specific cancer cell type based on their miRNA expression patterns. In particular, it opens up new opportunities to implement Boolean logic for lowabundance nucleic acids in living cells that would be difficult to detect with conventional molecular circuits. The reported intelligent sensor could be potentially reconfigured to accommodate miRNA patterns of different cancer cell types for advanced miRNA analysis. Experimental Section Preparation of GQN (A1-QD-A2) 150 µL of DNA1-GNPs and 250 µL of DNA1-GNPs were treated with MCH (100 µM) at 1:2000


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molar ratio for 1 hour at room temperature. The resulting GNPs were purified via centrifugation at 14000 rpm for 8 min and recovered in 150 µL and 250 uL 1× PBS respectively. The MCH-treated DNA1-GNPs and DNA2-GNPs were mixed with Linker-1 and Linker-2 DNA at 1:60 molar ratio respectively in the presence of 50 mM NaCl and 2.5 mM MgCl2. The solution was heated at 60 °C for 10 min and then slowly cooled to room temperature and left for 6 hours. Free DNA was removed via centrifugation twice at 14000 rpm for 8 min. The obtained GNPs were recovered in 150 µL and 250 uL 1× PBS respectively and the GNPs concentrations of each sample were quantitated. Afterwards, the above GNPs solutions were combined at desired molar ratio, and then mixed with DNA3-QDs at A1/A2/QD molar ratio of 3:5:100 in the presence of 50 mM NaCl and 2.5 mM MgCl2. The solution was incubated at 37 °C for 3 hours and then slowly cooled to room temperature. The GQN was purified twice via centrifugation at 6000-8000 rpm for 3 min, and resuspended in 400 µL 1× PBS. Catalytic disassembly of GQN (A1-QD-A2) Catalytic disassembly was initiated by incubating GQN containing 5 nM GNPs with 37.5 nM Fuel-1, 25 nM Fuel-2, and C’-1/C’-2 with different molar ratios of C’-1/Linker-1 and C’-2/Linker-2 (1, 0.1, 0.05, 0.0375, 0.025, 0.02, 0.01, 0.005, 0.001 and 0, respectively) at 37 °C for 6 hours. Samples were centrifuged once at 14000 rpm for 3 min. The pellet was redispersed in 1× PBS and characterized with agarose gel electrophoresis. The photoluminescence spectra of the disassembled QDs in the supernatants were recorded. Fluorescence imaging of living cells Huh-7 cells, MCF-7 cells, and HEK-293 cells were seeded into an 8-well chamber slide (Thermal Fisher) at a density of 2×104 cells per well. After incubation for 24 hours, the cells were washed once with 1× PBS, a 200 µL mixture containing 37.5 nM Fuel-1, 25 nM Fuel-2, 1.2 µL Lipofectamine-2000, and GQN (5 nM GNPs) in DMEM was added into each well and incubated at 37 °C in a humidified incubator for 6 hours. Then 4 ul DAPI (10 µg/mL) were added and the cells were incubated for 30 min. Cells incubated with GQN and Lipofectamine-2000 in the absence of fuel DNA were used as controls. After washed twice with 1× PBS, the fluorescence images of the cells were captured on a Leica TCS SP5 II confocal laser scanning microscope using a 63× oil immersion objective. DAPI and QDs were excited at 405 nm. And their emission signals were collected between 450 and 500 nm, and between 580 and 650 nm, respectively. PL of 60 cells in four randomly selected regions was measured and


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averaged using ImageJ software. Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Experiment details; TEM and Abs spectrum of GNP (S1); TEM, Abs and PL spectra of DNA-QD (S2); PAGE and GFC for QD and DNA assembly (S3); AGE for GQN assembly (S4); Statistics of the number of GNPs in each GQN complex (S5); PL spectra and AGE for optimization of A1:A2:QD molar ratio (S6); AGE for catalytic disassembly of GQN (S7); PL recovery rate for catalytic disassembly of A1-QD with different toehold lengths (5 nt and 7 nt) (S8); AGE for GQN disassembly under different F/L molar ratios (S9); qPCR data for miRNA quantification (S10 and Table S1); stability of GQN against DNase I (S11); cell viabilities after GQN treatment (S12). Acknowledgements This work was supported in part by the NSFC (21475093, 21522506), the National High-Tech R&D Program (2014AA020518), 1000-Young Talents Plan, PAPD, and startup funds from Soochow University. References 1. Silva, A. P. D.; Uchiyama, S. Molecular Logic and Computing. Nat. Nanotechnol. 2007, 2, 3990. 2. Li, J.; Green, A. A.; Yan, H.; Fan, C. Engineering Nucleic Acid Structures for Programmable Molecular Circuitry and Intracellular Biocomputation. Nat. Chem. 2017, 9, 1056-1067. 3. Seelig, G.; Soloveichik, D.; Zhang, D. Y.; Winfree, E. Enzyme-Free Nucleic Acid Logic Circuits. Science. 2006, 314, 1585-1588. 4. Wang, F.; Lu, C. H.; Willner, I. From Cascaded Catalytic Nucleic Acids to Enzyme-DNA Nanostructures: Controlling Reactivity, Sensing, Logic Operations, and Assembly of Complex Structures. Chem. Rev. 2014, 114, 2881-2941. 5. Stojanovic, M. N.; Stefanovic, D.; Rudchenko, S. Exercises in Molecular Computing. Acc. Chem. Res. 2014, 47, 1845-1852. 6. Groves, B.; Chen, Y. -J; Zurla, C.; Pochekailov, S.; Kirschman, J. L.; Santangelo, P. J.; Seelig, G. Computing in Mammalian Cells with Nucleic Acid Strand Exchange. Nat. Nanotechnol. 2016, 11, 287-294. 7. Hemphill, J.; Deiters, A. DNA Computation in Mammalian Cells: MicroRNA Logic Operations. J. Am. Chem. Soc. 2013, 135, 10512-10518. 8. Elbaz, J.; Lioubashevski, O.; Wang, F.; Remacle, F.; Levine, R. D.; Willner, I. DNA Computing Circuits using Libraries of DNAzyme Subunits. Nat. Nanotechnol. 2010, 5, 417-22. 9. Jung, C.; Ellington, A. D. Diagnostic Applications of Nucleic Acid Circuits. Acc. Chem. Res.


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Scheme 1. Schematic illustration of GQN for molecular computation of dual miRNA in living cells. (a) GQN-based AND logic gate integrated with two signal amplifiers for logic operation of miRNA-21 and miRNA-122 in living cells; (b) Synthetic route of GQN; (c) DNA and microRNA sequences for GQN construction and logic operation.


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Figure 1. Characterization of GQN. (a) Low magnification and (b) high magnification TEM images of GQN; (c) EDX spectroscopy of GQN; (d) DLS measurement of GQN.


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Figure 2. In vitro logic operation of two catalyst DNA C’-1 and C’-2 using GQN. (a) Agarose gel electrophoresis characterization of catalytic disassembly of GQN induced by C’-1 (0.05×) and C’-2 (0.05×) in the presence of fuel DNA F-1 (1×) and F-2 (1×); (b) Photoluminescence spectra and (c) photoluminescence intensities of released QDs after each disassembly reaction induced by C’-1 (0.05×) and C’-2 (0.05×) in the presence of fuel DNA F-1 (1×) and F-2 (1×). (The error bars represent standard deviation)


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Figure 3. Titration experiments for double catalyst DNA C’-1 and C’-2. (a) Photoluminescence spectra of QDs released from disassembled GQN under different C’/L (C’-1/L-1 and C’-2/L-2) molar ratios in the presence of fuel DNA F-1 and F-2; (b) Calibration curves for C’-1 and C’-2 titration without fuel DNA (the error bars represent standard deviation and are determined according to three trials of each experiment); (c) Photoluminescence spectra of QDs released from disassembled GQN under different C’/L (C’-1/L-1 and C’-2/L-2) molar ratios in the absence of fuel DNA F-1 and F-2.


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Figure 4. Titration experiments for single catalyst DNA C’-1 or C’-2. (a) Photoluminescence spectra of QDs released from disassembled GQN under different C’-1/L-1 molar ratios in the presence of fuel DNA F-1 (C’-2/L-2 molar ratio was kept constant at 1); (b) Calibration curve for C’-1 titration; (c) Photoluminescence spectra of QDs released from disassembled GQN under different C’-2/L-2 molar ratios in the presence of fuel DNA F-2 (C’-1/L-1 molar ratio was kept constant at 1); (d) Calibration curve for C’-1 titration; (e) Quantitative analysis of miRNA-21 and miRNA-122 (copies/cell) extracted from HEK-293, MCF-7, and Huh-7 cells using calibration curves shown in (b) and (d). (The error bars in (b) and (d) represent standard deviation and are determined according to three trials of each experiment)


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Figure 5. TEM images of HEK-293, MCF-7, and Huh-7 cells incubated with GQN and fuel DNA for 1 hour and 6 hours. The black dots are GNPs.


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Figure 6. Logic operation of miRNA-21 and miRNA-122 in living cells. (a) Fluorescence images of living HEK-293, MCF-7, and Huh-7 cells transfected with GQN in the presence or absence of fuel DNA for intracellular logic operation of miRNA-21 and miRNA-122. (b) Mean PL intensities of HEK293, MCF-7, and Huh-7 cells transfected with GQN in the presence or absence of fuel DNA (the error bars represent standard deviation).


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TOC Figure


Logic Sensing of MicroRNA in Living Cells Using DNA-Programmed

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