Enzyme-Free Nucleic Acid Amplification Assay Using a Cellphone

Subscriber access provided by READING UNIV

Technical Note

Enzyme-free Nucleic Acid Amplification Assay Using a Cellphone-Based Well Plate Fluorescence Reader Donghyuk Kim, Qingshan Wei, Dong Hyeok Kim, Derek Tseng, Jingzi Zhang, Eric Pan, Omai Brandt Garner, Aydogan Ozcan, and Dino Di Carlo Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b03848 • Publication Date (Web): 14 Nov 2017 Downloaded from http://pubs.acs.org on November 14, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Enzyme-free Nucleic Acid Amplification Assay Using a Cellphone-Based Well Plate Fluorescence Reader Donghyuk Kim1, Qingshan Wei2, Dong Hyeok Kim1, Derek Tseng4, Jingzi Zhang,4 Eric Pan,4 Omai Garner3, Aydogan Ozcan4, and Dino Di Carlo1 Department of Bioengineering, University of California, Los Angeles1 Department of Chemical and Biomolecular Engineering, North Carolina State University2 Department of Pathology and Laboratory Medicine, University of California, Los Angles3 Department of Electrical Engineering, University of California, Los Angeles4 ABSTRACT: Nucleic acids, DNA and RNA, provide important fingerprint information for various pathogens and have significant diagnostic value; however, improved approaches are urgently needed to enable rapid detection of nucleic acids in simple point-ofcare formats with high sensitivity and specificity. Here, we present a system that utilizes a series of toehold-triggered hybridization/displacement reactions that are designed to convert a given amount of RNA molecules (i.e., the analyte) into an amplified amount of signaling molecules without any washing steps or thermocycling. Fluorescent probes for signal generation were designed to consume products of the catalytic reaction in order to push the equilibrium and enhance the assay fold amplification for improved sensitivity and reaction speed. The system of toehold-assisted reactions is also modeled to better understand its performance and capabilities, and we empirically demonstrate the success of this approach with two analytes of diagnostic importance, i.e., influenza viral RNA and a micro RNA (miR-31). We also show that the amplified signal permits using a compact and cost-effective smartphone-based fluorescence reader, an important requirement towards a nucleic acid based point-of-care diagnostic system.

Point-of-care (PoC) diagnostics can facilitate immediate action to inform patient care for emergent conditions in which every hour affects patient health outcomes. The portability and cost of such systems can democratize the delivery of healthcare beyond central hospitals with significant infrastructure. There are several critical technical challenges to achieve PoC diagnostics because complex biological samples (e.g. blood, serum, sputum, etc.) often require substantial sample preparation and assay operation often makes use of bulky instrumentation to prepare, run, and read out the assay. Irregularities in the levels and sequences of DNA and RNA have been well documented for a variety of physiological abnormalities from inflammation or infection to cancer malignancy;1,2 thus, nucleic acids can be among the most important biomarkers for disease diagnosis. However, the low level of abnormal mutant or pathogen-derived nucleic acids necessitates some amplification process in most cases to obtain a sensitive readout. Polymerase chain reaction (PCR), despite its complexity, is the quintessential example of a nucleic acid amplification assay with tremendous uses in clinical laboratories thanks to its performance and well-established theoretical/empirical understanding of the method. However, PCR procedures are not easily automated in small footprint laboratory equipment, which exacerbates the technical challenges in achieving PoC diagnostics. There has been recent progress in miniaturizing and automating nucleic acid amplification tests in small benchtop commercial systems (e.g. the Alere i system from Alere and cobas Liat System from Roche). Such progress has resulted in very sensitive and automated diagnostic systems; however, there is still a need for specialized equipment and enzyme-based nucleic acid amplification, in some cases utilizing multiple types of enzymes leading to increased cost and reduced portability of the assay. There have been efforts in developing more field-friendly detection schemes for nucleic acids, such as molecular beacon-based DNA probes that directly bind to nucleic acids of interest; however, because no amplification process is incorporated, the sensitivity of the

probes were typically far less than a typical clinical need (subnM level limit-of-detection).3–7 We present an oligonucleotide-based molecular assay to achieve enzyme-free amplification of a nucleic acid signal,8,9 and combine this approach with a smartphone-based portable fluorescence plate reader platform as a potential solution for nucleic acid based PoC diagnostics. The oligonucleotide machine involves multiple steps of toehold-triggered hybridization/displacement reactions, the equilibrium of which is designed to be further pushed by consuming products during signal generation. More precisely, the analyte single-stranded nucleic acid molecule (i.e., influenza RNA or miR-31 in this study, Analyte in Figure 1) toehold binds to a multiplex substrate (MS, Figure 1), which displaces a first output sequence 1 (OS1) by a branch migration, opening a region for hybridization on the MS for dummy sequence (DS)-binding. Binding of DS displaces both OS2 and Analyte again via branch migration completing a cycle where Analyte is free again to initiate another catalytic cycle. This complex system of reactions occurs in a single homogenous mixture and thus individual parameters must be well understood for precise control over the reaction. We investigate and optimize key parameters through a model and demonstrate it empirically using two different nucleic acid target analytes, influenza viral RNA and microRNA miR-31. We also employ a compact and costeffective smartphone-based fluorescence reader10,11 for rapid quantification of enzyme-free nucleic acid amplification assay performed in a standard 96 well plate. Our results show that the final platform (combining oligonucleotide-based molecular machinery and a smartphone-based fluorescence reader) provides orders of magnitudes sensitivity improvement compared to conventional lateral flow assays. This integrated and costeffective readout approach presents significant advantages for the development of new PoC diagnostic systems. RESULTS AND DISCUSSION

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 6

Figure 1. Schematic of the molecular machine. Theoretical exploration of the impact of draining individual or both outputs in the catalytic cycle. The DNA molecular machinery translates a molecular input (analyte nucleic acids, or analyte in Figure 1) into an amplified amount of two different single-stranded output sequences (output sequence, OS, 1 and 2 in Figure 1), through a complex set of reactions which we investigate with a mathematical model. The model considers two key reaction components that affect the baseline signal; it considers the leak reaction (k0) that occurs without the presence of analyte and the initial self-dissociation due to equilibrium of the MS alone that generates free OS1 and OS2. To account for the MS self-dissociation, we estimate ~ 1% of the initial MS, that is, 10nM out of 1µM MS self-dissociates based on the ∆G of the MS complex (Supplementary Figure 2). We first investigate designs of the system to consume OS1 or OS2 alone, or both OS1 and OS2 as depicted in Figure 2a (Supplementary Figure 1 illustrates these separate scenarios for generating a signal). OS1 and OS2 will interact with oligonucleotides OS1- or OS2-specific dyes (OD1 or OD2) respectively to generate a fluorescent signal, and thus, sequestering these molecules will have an impact on the overall catalytic cycle that generates OS1 and OS2. We also consider xOD1 and xOD2, the same OD1 and OD2 probes without the capability of generating fluorescent signal (marked as Test Species in Figure 1), to separately examine the impact of sequestering OS1 and OS2 to the overall signal generation. The model predicts the benefits of consuming both OS1 and OS2 for signal generation. Figure 2b-c demonstrates that the model is predictive, and more importantly, consuming both OS1 and OS2 generates the largest signal, which will significantly benefit use of low cost readout modalities. We next investigated other quantitative aspects of the reaction using the model: amplification power, signal linearity as a function of concentration, and signal-to-background ratio. The amplification power of the catalytic cycle (i.e., the ratio of the # of fluorescent signaling molecules generated in the analyteinitiated reaction to the # of analyte molecules at a given time point) was higher with both OS1+OS2 generating signal (light color) compared to the OS1 scenario (dark color), at all given concentrations of analyte and reaction times. Because, unlike PCR, the system has an upper limit possible for signal generation (a maximum of the [MS] to be consumed by analyte), the predicted amplification power begins to decrease as [analyte]

approaches the [MS]. As an extreme example, for 1 µM of MS at a 10-minute time point the estimated amplification power of the catalytic cycle with 100fM analyte is 502.07, but considering 100nM analyte it reduces to 172.93. Consistent with the amplification power varying with concentration, the model predicted varying linearity of the generated signal with [analyte], even at low [analyte] ranges where amplification power remains relatively uniform (Figure 2e). Using the model, we examined 5 different concentration ranges (R1: 100fM – 100pM, R2: 100fM – 1nM, R3: 100fM – 10nM, R4: 1pM – 10nM, and R5, 10pM – 10nM). The linearity of the catalytic cycle generating fluorescence signal in response to [analyte] varied depending on [analyte] and time (Figure 2e). Overall, consuming OS1+OS2 showed a better linearity than OS1 alone throughout the concentration ranges (data not shown). We investigated the impact of consuming OS1 alone and OS1+OS2 on signal to background levels of generated fluorescence. The background was predicted to generate 1.4 x 1012 and 2.8 x 1012 signaling molecules in OS1 alone and OS1+OS2 respectively, which will directly contribute to the limit-of-detection of the developed catalytic cycle. However, as there are other practical aspects to consider, such as the accuracy of fluid delivery, timing, and the optical system, that are difficult to model, we only provide estimated signal foldchange at varying [analyte] which is expected to correlate with limit of detection when combined with these other factors. Estimated signal fold-change is predicted to increase above 5% of background level at 4pM and continue to rise for concentrations above this level (Supplemental Figure 3a). Lastly, the impact of the stoichiometry of individual reaction components on the overall performance of the catalytic cycle was examined using the model (Supplementary Figure 3b). For 100fM analyte we examined the number of produced signaling molecules for varying stoichiometry. At a 1:1 ratio of MS and DS (1µM each), at 10 minutes, the cycle produced 1.5 x 109 and 3.0 x 109 signaling molecules by OS1 alone and OS1+OS2, respectively. At a 10:1 ratio of MS and DS (1µM:100nM), the cycle produced 1.5 x 109 and 3.0 x 109 suggesting a similar level of performance; however, at 1:10 ratio of MS and DS (100nM:1µM), the cycle generated 1.5 x 108 and 3.0 x 108 signaling molecules suggesting the importance of MS over DS in the cycle.


Page 3 of 6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. (a) Reaction schemes examined. (b) Simulation and experimental data (Pearson correlation coefficient: 0.94, 0.92, 0.97, 0.97, and 0.99 for Case 1 through 5, respectively). Analyte is synthetic influenza RNA at 10nM. (c) Signal generation by OS1 alone (Case1, Blue) and OS1+OS2 (Case5, Red) at varying time and analyte concentrations. (d) Amplification power of the catalytic cycle at varying time and analyte concentrations. Amp. Power: Amplification Power = (the # of the produced signaling molecules by analyte - the # of signaling molecules generated by zero analyte) / the # of analyte molecules. Dark color: OS1 alone (Case1). Light color: OS1+OS2 (Case5) (e) Signal linearity with respect to analyte concentrations. R1: 100fM – 100pM, R2: 100fM – 1nM, R3: 100fM – 10nM, R4: 1pM – 10nM, and R5, 10pM – 10nM. Dark color: OS1 alone (Case1). Light color: OS1+OS2 (Case5). Molecular assay to detect influenza viral RNA and miR-31. Based on the above simulation data, we characterized the performance of the oligonucleotide-based molecular machinery for two molecular assays to detect influenza viral RNA and miR-31. We used a synthetic RNA as analyte and the circuit was designed to identify a 144-167 nucleotide position on the influenza viral RNA, and the full sequence of the mature miR31 (Supplementary Table 1).12–14 Figure 3a shows the time-dependent signal increase by influenza viral RNA in comparison of consuming only OS1 to consuming both OS1 and OS2 for signal generation. The OS1+OS2 cases resulted in a significantly higher signal-tonoise ratio compared to the OS1 for the same analyte concentration as predicted by the model. Similar reaction kinetics was observed for miR-31 detection, as well. (Figure 3d and Supplementary Figure 5). With regard to the limit-of-detection, OS1 alone identified the presence of the target RNA above background levels for > 1 nM concentrations;15 however, sub-pM levels can be measured above background by sequestering both OS1 and OS2 (Figure 3b). We performed similar experiments for both influenza viral RNA and miR-31 as analyte, and achieved successful detection of ~ 100 fM within 10 minutes of assay time (Figure 3b, e) for both analytes. While this is promising, this sensitivity may be difficult to obtain in a clinical setting where

one can expect use of a low cost readout instrument or significant variations in pipetting, reagent storage, etc. Also, this empirical observation deviated significantly from model prediction (signal fold-change 1.002 at [Analyte] = 100fM). We believe this discrepancy arises because the model assumes that all individual sequences are orthogonal to each other. In reality, for example, analyte can bind to the dangling end of OD1, or DS can bind to the dangling end of both OD1 and OD2, which may make all reactants locally concentrated rather than homogenously spread out in solution. This possibility was explored by adjusting modeling parameters (k constants or concentrations of individual components), which resulted in 1.01 ~ 1.05 predicted signal fold-changes with [Analyte] = 100fM. This aspect is inevitable, and the level of its contribution will vary depending on sequences – it will be noteworthy to minimize such competing interactions, particularly in the key recognition zones (e.g., toe-hold binding regions). The OS1 + OS2 scheme at a 10 minute time point seems to assist the quantification performance (i.e., linearity) of the assay as predicted in the model – compared to the OS1 alone, (O1 alone: R2 = 0.23 at 10 minutes) OS1+OS2 scheme provided a significantly improved quantification efficiency (OS1+OS2 together: R2 = 0.71 at 10 minutes). Lastly, we applied the developed assay to more clinically relevant sample matrices. For miR-31, we performed two dif-



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 6

ferent sets of experiments. First, we spiked the synthetic version of miR-31 into human serum from healthy donors. As seen in the Figure 3, we were able to successfully detect miR31 with 100 fM limit-of-detection at 10 minutes. Next, as abnormalities in cellular miR-31 levels are often associated with a variety of diseases,7,14,16 we tested the approach in detecting cellular miRNAs.

Figure 4. Molecular analysis of influenza RNA on a cellphone reader. (a,b) Schematic and image of the cellphone-based 96 well plate reader.[ACS Nano 2017, ACS Nano 2015] Performance comparison for influenza RNA detection by (c) a conventional plate reader and (d) the developed cellphone-based reader.

Figure 3. Performance data. (a) Kinetic study of the OS1-only and OS1+OS2 cases using influenza viral RNA as an analyte. (b) Machinery performance with varying concentrations of influenza viral RNA (OS1+OS2, read at 10 minutes at room temperature). (c) Machinery performance with clinically-isolated influenza samples. Each samples were diluted 1000x for examination using the OS1+OS2 scheme and read after 10 minutes at room temperature. (d) Kinetic study of the OS1+OS2 scheme detecting a microRNA (miR-31). (e) Machinery performance with varying concentrations of miR-31 (OS1+OS2, read at 10 minutes at room temperature). (f) Machinery performance with miR-31 spiked in human serum samples. To note, “Fluorescence*” is defined as Fluorescence generated by analyte – Fluorescence generated with no analyte added. See Supplementary Figure 4 for data before subtraction. We assayed for miR-31 spiked into lysates of two cell types, HeLa SW480, using the OS1 + OS2 scheme. The results indicate successful identification of miR-31 at 100 pM in the cell lysates (Supplementary Figure 3). To note, miR-31 level in serum samples of healthy individuals and the two tested cell types are expected to be negligible.17–21 For influenza, two clinical influenza positive and negative samples were examined using our technology. The total nucleic acid extracted from patient nasopharyngeal swab samples were obtained from the clinical microbiology laboratory at the University of California, Los Angeles, and diluted 1000x before examination. The oligonucleotide machinery successfully distinguished the positive sample in comparison to the negative sample. While more clinical samples will need to be examined, these initial experiments demonstrate the potential of the developed machinery to be applied to more complex samples that are clinically relevant. Cell-phone based well-plate reader for analysis of nucleic acids in a point-of-care format. Conventional lateral flow assays used for point-of-care diagnostics have a typical LOD of hundreds nM which is not sufficient to accurately identify

the presence of the majority of nucleic acid disease markers.2,19,20,22 As the developed oligonucleotide machinery demonstrates outstanding performance for the detection of nucleic acids in a point-of-care setup (quick, no washing, no thermocycling while achieving a sub-pM limit-of-detection), over a wide range of temperatures (15 ~ 35℃)9, we employed a smartphone-based field-portable fluorescence reader (Fig. 4a,b) to create a complete point-of-care diagnostic system. With this mobile platform, we performed assays for varying concentrations of synthetic influenza viral RNA using the oligonucleotide machinery and monitored signal generation from individual conditions using both a conventional plate reader and the developed smartphone reader. As shown in Figure 4, the smartphone reader detected the target signal above the background down to 1pM of synthetic influenza RNA. To note, signal generation plateaus out with analyte concentration due to the preset capacity of the reaction (1 µM individual oligonucleotides). This detection limit is orders of magnitude lower than conventional lateral flow POC tests; however, this may be insufficient to detect disease markers at extremely low quantity. We previously demonstrated potential improvement when implementing the oligonucleotide machine assays in compartmentalized volumes (digital format),9 which may benefit future applications. CONCLUSIONS We developed an oligonucleotide-based molecular machine with a model that validates its utility and optimize several key parameters for nucleic acid detection. The optimized molecular circuit achieved amplification power of ~103 and sub-pM sensitivity at room temperature within 10 minutes without any washing steps, demonstrating its utility in a PoC setup. The implementation of the developed molecular machine on the smartphone reader demonstrated its potential as a true PoC diagnostics achieving orders of magnitudes higher sensitivity compared to currently available homogenous systems (Table 1). The developed machine poses additional benefits for PoC diagnostic applications because enzymes are not needed for operation while maintaining a homogenous assay. Any necessary requirements for storing or using enzymes such as refrigeration or operation temperature control can be mitigated.


Page 5 of 6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Technology

Analysis Time (Minutes)

Sample Volume (µL)

Amplification

Limit-ofdetection

Easy Procedure (One-step?)

Adaptable with other assays /readouts

Hand-held

Sub-µM

Possible

No

Yes

Sub-µM

Yes

No

No

Sub-pM Sub-fM Sub-fM

Yes No Yes

Yes No No

Yes Possible No

Lateral flow >30 >100 N/A biosensors Molecular bea>20 100 2n cobasLiat 30 >100 2n Table 1. Comparison of conventional assays/assay kits.3,23–27 Custom oligonucleotide synthesis, with/without base modifications, is getting more and more accessible at lower costs for end-users, which also promises additional economic benefits for the developed technology. Even with amplification, some targets of interests, such as circulating tumor DNAs (ctDNAs) for example, are present in an extremely low quantity which requires orders of magnitude higher sensitivity and better quantification performance than this approach provides. As briefly discussed at the end of result section, a digital assay platform may be suitable to improve performance for these types of targets. Our previous study suggests that the assay is compatible with a microfluidic digital assay platform which provides benefits for both sensitivity and quantification.9 In short, this approach clearly demonstrates the potential for diagnostic applications of many different diseases leading to low-cost PoC systems.

(11)

(12)

(13) (14)

(15) (16)

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website.

AUTHOR INFORMATION

(17) (18)

Corresponding Author Dino Di Carlo, Ph.D. [email protected]

ACKNOWLEDGMENT This work was support by NSF Grant #1332275.

REFERENCES (1) (2) (3) (4)

(5)

(6) (7) (8) (9) (10)

(19) (20)

Barber, G. N. Nature Reviews Immunology 2015, 15 (12), 760–770. Alarcón-Riquelme, M. E. Nature Genetics 2006, 38 (8), 866–867. Tyagi, S.; Kramer, F. R. Nature Biotechnology 1996, 14 (3), 303–308. Kang, X.; Li, Y.; Fan, L.; Lin, F.; Wei, J.; Zhu, X.; Hu, Y.; Li, J.; Chang, G.; Zhu, Q.; Liu, H.; Yang, Y. Virology journal 2012, 9, 56. Lee, C. K.; Lee, H. K.; Loh, T. P.; Lai, F. Y. L.; Tambyah, P. A.; Chiu, L.; Koay, E. S. C.; Tang, J. W. Emerging Infectious Diseases 2011, 17 (2), 287–290. Creemers, E. E.; Tijsen, A. J.; Pinto, Y. M. Circulation Research 2012, 110 (3). Xu, W.; Mezencev, R.; Kim, B.; Wang, L.; McDonald, J.; Sulchek, T. PLoS ONE 2012, 7 (10), e46609. Zhang, D. Y.; Turberfield, A. J.; Yurke, B.; Winfree, E. Science (New York, N.Y.) 2007, 318 (5853), 1121–1125. Kim, D.; Garner, O. B.; Ozcan, A.; Di Carlo, D. ACS Nano 2016, 10 (8), 7467–7475. Kong, J. E.; Wei, Q.; Tseng, D.; Zhang, J.; Pan, E.; Lewinski, M.; Garner, O. B.; Ozcan, A.; Di Carlo, D. ACS Nano 2017, 11, 2934-2943.

(21) (22)

(23)

(24) (25) (26)

(27)

Berg, B.; Cortazar, B.; Tseng, D.; Ozkan, H.; Feng, S.; Wei, Q.; Chan, R. Y.-L.; Burbano, J.; Farooqui, Q.; Lewinski, M.; Di Carlo, D.; Garner, O. B.; Ozcan, A. ACS Nano 2015, 9 (8), 7857–7866. Wenzel, J. J.; Panning, M.; Kaul, K. L.; Mangold, K. A.; Revell, P. A.; Luna, R. A.; Zepeda, H.; Perea, L.; Vazquez-Perez, J. A.; Young, S.; Rodic-Polic, B.; Eickmann, M.; Drosten, C.; Jilg, W.; Reischl, U. Journal of clinical microbiology 2010, 48 (9), 3088–3094. Gascoigne, K. E.; Taylor, S. S. Cancer Cell 2008, 14 (2), 111–122. Sun, D.; Yu, F.; Ma, Y.; Zhao, R.; Chen, X.; Zhu, J.; Zhang, C.-Y.; Chen, J.; Zhang, J. The Journal of biological chemistry 2013, 288 (13), 9508–9518. Zhang, D. Y.; Turberfield, A. J.; Yurke, B.; Winfree, E. Science 2007, 318 (5853). Yan, S.; Xu, Z.; Lou, F.; Zhang, L.; Ke, F.; Bai, J.; Liu, Z.; Liu, J.; Wang, H.; Zhu, H.; Sun, Y.; Cai, W.; Gao, Y.; Su, B.; Li, Q.; Yang, X.; Yu, J.; Lai, Y.; Yu, X.-Z.; Zheng, Y.; Shen, N.; Chin, Y. E.; Wang, H. Nature Communications 2015, 6, 7652. Sá Nchez, Y.; Huarte, M. . Alder, H.; Taccioli, C.; Chen, H.; Jiang, Y.; Smalley, K. J.; Fadda, P.; Ozer, H. G.; Huebner, K.; Farber, J. L.; Croce, C. M.; Fong, L. Y. Y. Carcinogenesis 2012, 33 (9), 1736–1744. Zhang, T.; Wang, Q.; Zhao, D.; Cui, Y.; Cao, B.; Guo, L.; Lu, S. H. Clinical Science 2011, 121, 437–447. Lee, E. J.; Baek, M.; Gusev, Y.; Brackett, D. J.; Nuovo, G. J.; Schmittgen, T. D. RNA (New York, N.Y.) 2008, 14 (1), 35–42. Yang, M.-H.; Yu, J.; Chen, N.; Wang, X.-Y.; Liu, X.-Y.; Wang, S.; Ding, Y.-Q. PLoS ONE 2013, 8 (12), e85353. Link, A.; Balaguer, F.; Shen, Y.; Nagasaka, T.; Lozano, J. J.; Boland, C. R.; Goel, A.; Jiang, B.; Naoe, T.; Padhye, S.; Sarkar, F. Cancer Epidemiology Biomarkers & Prevention 2010, 19 (7), 1766–1774. Buckhout-White, S.; Spillmann, C. M.; Algar, W. R.; Khachatrian, A.; Melinger, J. S.; Goldman, E. R.; Ancona, M. G.; Medintz, I. L. Nature communications 2014, 5, 5615. Cuendet, M.; Mesecar, A. D.; DeWitt, D. L.; Pezzuto, J. M. Nature protocols 2006, 1 (4), 1915–1921. McManus, S. A.; Li, Y. Journal of the American Chemical Society 2013, 135 (19), 7181–7186. Vashist, S. K.; Luppa, P. B.; Yeo, L. Y.; Ozcan, A.; Luong, J. H. T. Trends in Biotechnology 2015, 33 (11), 692–705. Kelley, S. O.; Mirkin, C. A.; Walt, D. R.; Ismagilov, R. F.; Toner, M.; Sargent, E. H. Nature Nanotechnology 2014, 9 (12), 969–980.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

For TOC only


Page 6 of 6

Enzyme-Free Nucleic Acid Amplification Assay Using a Cellphone

Recommend Documents