Ultraspecific Multiplexed Detection of Low-Abundance Single

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Ultra-specific multiplexed detection of low-abundance single-nucleotide variants by combining masking tactic with fluorescent nanoparticle counting Xiaojing Pei, Tiancheng Lai, Guangyu Tao, Hu Hong, Feng Liu, and Na Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00685 • Publication Date (Web): 05 Mar 2018 Downloaded from http://pubs.acs.org on March 6, 2018

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Analytical Chemistry

Ultra-specific multiplexed detection of low-abundance singlenucleotide variants by combining masking tactic with fluorescent nanoparticle counting Xiaojing Pei, Tiancheng Lai, Guangyu Tao, Hu Hong, Feng Liu, and Na Li* Beijing National Laboratory for Molecular Sciences (BNLMS), Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Institute of Analytical Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, P. R. China Correspondence should be addressed to Dr. Na Li. Tel: +8610 62761187; email: [email protected].

ABSTRACT: To be able to detect simultaneously multiple single-nucleotide variants (SNVs) with both ultra-high specificity and low-abundance detectability is of pivotal importance for molecular diagnostics and biological researches. In this contribution, we for the first time developed a multiplex SNV detection method that combines the masking tactic with fluorescent nanoparticle (FNP) counting based on the sandwich design. The method presents the rivaling performance due to the advantageous features: the masking reagent was designed to hybridize with the extremely large amount of the wild-type sequence to render the assay a high specificity; FNP counting provides a sensitive multiplexed SNV detection; the sandwich design facilitates an easy separation to make the detection free of interferences from the matrix. For single SNV target discrimination, including 6 most frequently occurring DNA and 2 possible RNA KRAS gene mutations as well as 11 artificial mutations, the discrimination factor ranged from 204 to 1177 with the median 545. Amongst tested 19 SNVs, as low as 0.05% abundance was successfully identified in 14 cases, and 0.1% abundance was identified for rest 5 cases. For multiplexed detection of SNVs in KRAS gene, as low as 0.05–0.1% abundances were achieved for multiple SNVs occurring at same and different codons. As low as 0.05% low-abundance detection sensitivity was also achieved on PCR amplicons of human genomic DNA extracted from cell samples. This proposed method presents the potential for ultra-high specific multiplexed detection of SNVs with low-abundance detectability, which may be applied to practical applications.

Single-nucleotide variants (SNVs), where a single base is substituted, inserted or deleted from a sequence of DNA or RNA, have been regarded as important molecular markers for the biomedical research and clinical applications.1-3 No single gene mutation occurs in all tumors, for instance, point mutations discovered in KRAS genes are closely associated with multi-types of cancers, and multiple mutations have been observed in the same codon (e.g., codon 12 or 13 only) or different codons (e.g., in codons 12 and 13 simultaneously).4-10 It is highly desirable to develop a simple platform that can realize ultra-high specific multiplex SNV detection at very low abundance in one test sample to potentially enable the practical applications for clinical diagnostics and biological researches. Many efforts have been devoted to multiplex SNV discrimination and detection, for example, the DNA sequencing,11,12 polymerase chain reaction (PCR),13-15 the microarray,16-19 enzyme-assisted methods,20-30 and hybridization-based 31-37 methods, where the discrimination readout can be optical,38-48 electrical, mass spectrometric or electrochemical signals.7,16,33,49-51 However, limited approaches can realize the multiplex SNV detection with both ultra-high specificity and sensitivity in a simple manner. To achieve the high discrimination factor and low-abundance detectability, most available

methods require elaborated and sophisticated probe or primer design, time-consuming procedures, and the tedious amplification step in the workflow.22,23,25,52-55 In the efforts toward enzyme-free, highly specific SNV discrimination, the emergence of competitive or masking systems sheds lights on the development of highly specific hybridization-based SNV discrimination technologies.20,24,31,33,34,56,57 Amongst, the masking or sequester hairpin is an extremely simple masking reagent which enables significantly enhanced SNV discrimination without complicating probe designs for the detection, because the hairpin structure will not interfere with the other probes in the test system; in combination with the molecular beacon for signal recognition and signal readout, the question is that high discrimination factors (>200) were not consistently achieved for the majority of all tested SNVs, and the requirement for dual labeling of the different fluorophore and quencher increases the reagent cost.31 Furthermore, based on common spectral measurements, it is hard to achieve the sensitive lowabundance detectability (e.g., A) from the cancer cell following PCR. We proved that this proposed method can be a simple approach for ultra-specific, highly sensitive multiplex SNV detection in practical applications. EXPERIMENTAL SECTION

Materials. All synthetic DNA oligonucleotides were purchased from Sangon Biotech Co., Ltd (Shanghai, China). All HPLC-purified miRNAs and RNase inhibitor were purchased from Takara Biotechnology Co., Ltd (Dalian, China). 1-Ethyl3-[3-dimethylaminopropyl] carbodiimide hydrochloride (EDC, 99%) and Triton X 100 (C.P.) were obtained from Sinopharm Chemical Reagent Co., Ltd (Beijing, China). Na2HPO4, NaCl, NaH2PO4, NaOH, all of A. R. grade, were obtained from Beijing Chemical Works (China). 2-(N-Morpholino) ethanesulfonic acid (MES) was obtained from J&K Chemical, Ltd (Beijing, China). DRM-700 CELL-VU® CBC hemacytometers were obtained from Advanced Meditech International, Inc (New York, USA). Diethylpyrocarbonate (DEPC)-treated water was purchased from VWR International Co., Ltd (Pennsylvania, USA). Wahaha® purified water was used throughout for all DNA study. The RNase-free environment was created throughout the experiments by using DEPC-treated water and RNase-free tips and tubes. Fluorescent nanoparticles (FNPs), FC02F/10308 with size of 200 nm, 10865 with size of 220 nm, and 11242 with size of 250 nm, were purchased from Bangs laboratories, Inc (Indiana, USA). Streptavidin-modified magnetic beads (DynabeadsTM MyOneTM Streptavidin T1, d =1 µm, 10 mg/mL) were purchased from Thermo Fisher Scientific (Massachusetts, USA). The Lambda Exonuclease M0262S was purchased from Thermo Fisher Scientific (Massachusetts, USA). The 10 mM phosphate buffer saline (PBS) containing 0.1% Triton X 100 was used throughout the study. Preparation of DNA-MB and DNA-FNP, and Fluorescence microscopic imaging of FNPs & Color image processing. These procedures are the same as previously described and are also provided in the Supporting Information for convenience.60 The discrimination factor (DF) study. To a series of Eppendorf tubes, added 5 µL of 500 nM SNV, WT, or water, respectively, and 5 µL of 10 µM MH as well as 15 µL of PBS. The mixture was brought to 85 °C. After 5 min, the mixture

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Figure 1. DFs for the 8 KRAS gene mutations including 6 most frequently occurring DNA and 2 possible RNA SNVs, and 11 artificial SNVs by the proposed method. Schematics on the top illustrates the rationale, with the arrows and the number indicating the region and location of the designed mismatched position on the signaling sequence; on the right are the representative images of green FNPs acquired for the reagent blank, WT, and SNV; the first digit in the notation of mutants denotes the location of the mismatched position designed on the signaling sequence. was cooled and kept at 37 °C for 30 min. Then, 20 µL of 2 mg/mL DNA-MBs were added, and the mixture was cooled from 37 °C to 29 °C with slow agitation to allow the hybridization with capture sequence on the MB. Afterwards, 5 µL of 29 pM green DNA-FNPs (in case of red DNA-FNPs, 58 pM was used) were added, and the mixture was cooled from 29 °C to 25 °C with slow agitation and kept at 25 °C for 60 min. After magnetic separation, the supernatant was removed and the nanocomplex was washed eight times with PBS. Finally, 100 µL of 0.15 M NaOH was added to de-hybridize and release FNPs from the sandwich nanocomplex. After 15 min, 5 µL of the supernatant was applied on the hemacytometers and the image was obtained with the fluorescence microscope, and counts of FNPs were obtained. The discrimination factor (DF) is defined as the SNV-toWT counts gain ratio with the same experimental conditions (DF = ∆CSNV/∆CWT, C denotes the counts), where ∆CSNV or ∆CWT are the counts difference between the SNV or WT and the reagent blank, respectively. Low-abundance assay of single SNV target. To a series of Eppendorf tubes, added 5 µL of 10 µM MH, 5 µL of 500 nM WT, 5 µL of PBS and 5 µL of SNV, such that the SNV-to-WT concentration ratios in percentages were 100%, 10%, 1%, 0.4%, 0.1%, 0.05% and 0%. The same procedure as described above was followed.

Low-abundance assay of multiplex SNV targets. To a series of Eppendorf tubes, added 5 µL of 10 µM MH, 5 µL of 500 nM WT, 5 µL of PBS and 5 µL of SNV targets, such that the SNV-to-WT concentration ratios in percentages for SNV targets were 100%, 10%, 1%, 0.4%, 0.1%, 0.05% and 0%. The same procedure as for the low-abundance assay of single SNV target was followed. For the duplex RNA target assay, 20 µL of 4 mg/mL DNA-MB and 5 µL of DNA FNPs (29 pM greens and 58 pM reds) were used. For the triplex SNV target assay, 20 µL of 6 mg/mL DNA-MB and 5 µL of DNA-FNPs (29 pM greens, 58 pM reds, and 80 pM blues) were used. Detection of KRAS gene mutation in PCR amplicons from synthetic targets and human genomic DNA. The human pancreatic ductal adenocarcinoma Panc-1 cell lines and human colon cancer HT-29 cell lines were incubated in complete medium (Dulbecco’s Modified Eagle’s Medium, supplemented with 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin) at 37 °C in atmosphere containing 5% CO2. The mutant-type genomic DNA (KRAS G12D (c.35G>A)) and the wild-type genomic DNA were extracted from Panc-1 cell lines and HT-29 cell lines, respectively, using a commercial kit from Tiangen Biotech Co. (Beijing, China). The extracted genomic samples were quantified by the NanoDrop 2000 Spectrophotometer from Thermo Fisher Scientific (Massachusetts, USA). Then, two extracted genomic DNAs from the cells were mixed at 100:0, 10:90, 1:99, 0.5:99.5, 0.1:99.9, 0.05:99.95 and 0:100 ratios to the total con-


Analytical Chemistry 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. Counts for KRAS gene mutations including 6 frequently occurring DNA (A-F) and 2 possible RNA mutations (G-H) at different abundances. centration of 4 ng/µL (50 µL), and amplified by PCR. Specifireagent (denoted as MH) to prevent the competitive binding of cally, to a 200 µL PCR tube, 25 µL of 2×PCR Master Mix, 2 the wild-type (WT) sequence with DNA-FNP to produce uninµL of 1 µM forward primers, 2 µL of 1 µM 5’-PO4 reverse tended signals. Specifically, the hairpin MH is designed to be primers, and 2 µL of 1 nM mixed synthetic single-stranded perfectly complementary to WT, but carries a singleDNA templates (total amount 2 fmol, 65 pg) were added and nucleotide mismatch with SNV. Benefiting from the thermobrought to 50 µL with water. For genomic DNA PCR, the 200 dynamic penalty of the single-base mismatch with SNV and ng mixed genomic sample was added. PCR procedure (94 °C the energy barrier for opening the stem of the hairpin, MH for 1 min, 55 °C for 30 s, 72 °C for 20 s, 30 cycles) was perprefers to hybridize with WT rather than SNV target to form a formed on S1000™ Thermal Cyclers from Bio-Rad Laboratonon-fluorescent sandwich structure with DNA-MB. For detecries, Inc. (Hercules, USA). After the PCR amplification, 5 tion of multiple mutation sequences, MH hybridizes with WT units of Lambda Exonuclease were added to digest the strand and the individual SNV target is assigned to the different colcontaining 5’-PO4 in the duplex products for 20 min at 37 °C ored DNA-FNP. Counts of FNPs are obtained after magnetic and inactivated at 85°C for 10 min. Low-abundance assay was separation and imaging with the fluorescence microscope. By performed subsequently with the same procedure as described employing the masking tactic to prevent the competitive bindabove using green FNPs for readout. ing of WT with the signaling DNA-FNP, the signal produced by SNV and WT can be very well differentiated. RESULTS AND DISCUSSION The proposed method can realize the ultra-specific SNV discrimination with very sensitive low-abundance detectability Scheme 1 illustrates the assay workflow for multiplex SNV due to the following features. First, the masking hairpin, MH, detection by combining the masking tactic with fluorescent reacts with WT to prevent the competition between the SNV nanoparticle counting based on sandwich assay. The recognitarget and WT in hybridization with the signaling probe setion of the target sequence is based on the formation of the quence; thus, an ultra-high discrimination factor can be typical sandwich-structured nanocomplex amongst the SNV achieved without complicating the probe design due to the target, the capture-DNA-functionalized magnetic bead (DNAproperly stable hairpin structure. Second, the high emission MB), and the signaling-DNA-functionalized FNP (DNAbrightness and photo-stability of multicolored FNPs guarantee FNP). A hairpin-structured sequence serves as the masking the sensitive multiplex SNV detection at the single particle

Figure 3. Counts for KRAS gene mutations with 3 SNVs in codon 12 (A), 3 SNVs in codons 12 and 13 (B), and 2 RNA SNVs in codon 12 (C) at different abundances.


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Analytical Chemistry level in a small sample volume. Third, the convenient sandwich design facilitates an easy separation to make the detection free of interferences from the matrix by integrating recognition, signal production, and separation into one reaction.

while the underlengthed duplex would reduce the stability of the formed sandwich structure. The best discrimination ability was achieved with the combination of the 15-nt capture DNAMB and the 12-nt probe DNA-FNPs (Figure S1C).

Figure 4. Detection of PCR amplicons of KRAS G12D (c. 35G>A) mutation from synthetic and genomic DNA samples. Experimental workflow and rationale (A), counts for post-PCR product of mixed synthetic DNA samples (B) and post-PCR products of mixed genomic DNA samples (C) at different abundances. In (A), the forward PCR primer sequence and reverse PCR primer binding sites are underlined. It has been reported that about 66% of pancreatic cancers, In order to achieve the high specificity and sensitivity for 52% of colorectal carcinomas, and 21% of lung cancer are SNV detection, we evaluated major factors which may influrelated to the KRAS gene mutations.5,9,10 Most frequently muence the point mutation discrimination performance using KRAS G12D (c.35G>A) (Figure S1). The discrimination factated sites were found in codon 12 (82%), codon 13 (17%) and tor (DF), defined as the SNV-to-WT counts gain ratio with the codon 61 (5%).6,8,63-65 To investigate the potential application same experimental conditions, is a measure of the discriminaof the proposed method in clinical diagnostics, we tested 6 tion ability between SNV and WT; a high DF value is essenmost frequently occurring DNA and 2 possible RNA SNVs in tial to assure the detection of low-abundance SNV. In this KRAS gene (Figure 1). We used the thermodynamic penalty, optimization effort, fluorescent intensity was measured with ∆∆ , as the measure to provide guidance for design of misthe FAM-labeled signaling DNA instead of the signaling matched sites on the signaling sequence. Based on the DNA-FNP to save the time spent on FNP functionalization in NUPACK calculation, ∆∆ for all types of mutations on preliminary study. First, length and sequence of the stem of Sites 4−9 of the signaling sequence is in the range of 4.6−9.9 the masking hairpin was evaluated, because they can influence kcal/mol which is significantly higher than that on the distal both the stability of the MH and the hybridization efficiency ends (Sites 1−3 and 10−12) of the signaling sequence (see the with WT. Using the 12-nucleotide (nt) capture DNA-MB and Supporting Information for detail, Table S3, Figure S2). the 12-nt signaling DNA-FNPs, the best DF was achieved with Therefore, mismatched sites can be selected with some freethe combination of four A:T and one C:G base pairs in the dom on the region with the high ∆∆ value. As can be seen stem (Figure S1A); over-stable stem may provide a unintendfrom Figure 1, this proposed method presented the excellent edly high energy barrier that cannot be easily opened to hydiscrimination ability with DF values ranging from 224 to 707 bridize with WT, compromising the masking ability of MH. with a median of 332 for these 6 most frequently occurring Second, the MH-to-WT concentration ratio is a significant DNA mutations and 2 RNA the KRAS gene. We further evalufactor that exerts influence on the discrimination ability. It is ated 11 artificial DNA SNV/WT sequence pairs on the signaldesired that WT is completely masked by excess MH without ing sequence (including 2 substitutions, 6 insertions and 3 jeopardizing the SNV signal; however, the discrimination abildeletions), and DF values ranged from 204 to 1177 with a ity can still be compromised at the high MH level even with median of 657. The achieved DF values are amongst the highthe proper energy barrier consideration. Using the same capest in the recently published results (Table S4), and to the best turing and signaling DNA sequences, i.e., 12-nt capture DNAof our knowledge, this is the first and most highly specific MB and the 12-nt signaling DNA-FNPs, the highest DF value methods for amplification-free detection of point mutations was achieved when MH was 20 fold greater than that of WT using sandwich design. (Figure S1B). Third, the number of base pairs of the formed Encouraged by the above results, we applied the method to sandwich structure was evaluated, because it can influence the the detection of low-abundance SNVs. First, we tested the stability of the sandwich structure and SNV discrimination KRAS gene mutations, including 6 most frequently occurring ability. The formed duplex on the DNA-MB is only for stabiDNA and 2 possible RNA SNVs (Figure 2). For KRAS G12D lizing the sandwich nanocomplex; length of the formed duplex (c.35G>A), KRAS G12A (c.35G>C), KRAS G13C (c.37G>T), on DNA-FNP is crucial to the discrimination ability; the overKRAS G13D (c.38G>A) and KRAS G13V (c.38G>T), the lengthed duplex would compromise the discrimination ability, counts can still be significantly distinguished from the blank



Figure 5. The rationale of the sandwich design by spectrofluorometry, FNP counting method, the further generalized design with FNP counting method (A); DFs of spectrofluorometry, FNP counting method, the further generalized design, respectively (B); counts from the detection of KRAS G12D (c.35G>A) using spectrofluorometry (C) and FNP counting with the further generalized design (D). with abundance as low as 0.05%. KRAS G12V (c.35G>T) and 0.1:99.9, 0.05:99.95 and 0:100 ratios to a total concentration 2 RNA mutations, KRAS G12D (c.35G>A) and KRAS G12V of 1.3 pg/µL (50 µL), and followed by PCR and enzymatic (c.35G>U), can be detected at 0.1% abundance. The results digestion to produce single-stranded amplicons. As can be well demonstrated the capability of the proposed method in seen from Figure 4B, the SNV target can be successfully dedetecting low-abundance SNVs. Furthermore, we tested 11 tected with the abundance as low as 0.05%. Encouraged by the artificial mutation including 2 substitutions, 6 insertions and 3 results, we applied the proposed method to SNV detection deletions to evaluate the low–abundance detectability for diffrom cell samples. It has been reported that Panc-1 cancer cell ferent SNV types. As can be seen in Figure S3, the 0.05% lines carry the mutant-type DNAs of KRAS G12D, and HT-29 abundance detectability was achieved for almost all the tested cancer cell lines carry wild-type DNAs.5,6,8-10 We extracted the cases except the “6,T Insertion” and the “6,Deletion” (0.1% genomic DNAs from both cell lines to obtain the mutation abundance). The above sensitive SNV discrimination ability is gene and the wild-type gene, respectively. The mutant gebetter than that of most recently reported non-amplification nomic DNAs were then diluted with wild-type genomic DNAs methods (Table S4). in the ratio of 100:0, 10:90, 1:99, 0.5:99.5, 0.1:99.9, 0.05:99.95 and 0:100 to obtain a series of samples with the In KRAS gene, multiple SNVs were observed in the same total concentration of 4 ng/µL (50 µL). The intended SNV codon (e.g., codon 12 or 13 only) or different codons (e.g., target was detected after PCR and enzymatic digestion of the simultaneously in codons 12 and 13).5,6,9 Herein, we investigenomic DNA mixtures, and 0.05% abundance sensitivity was gated the potential of the proposed method for multiple mutaachieved (Figure 4C).These results firmly demonstrated the tion detection. For 3 mutations in codon 12, KRAS G12D potential of the proposed method in practical sample detection (c.35G>A), KRAS G12A (c.35G>C), and KRAS G12V without post-PCR treatments. (c.35G>T), 0.05−0.1% abundances were identified (Figure The proposed method possesses the practical adaptability 3A); for 3 G>T mutations in codons 12 and 13, KRAS G12V and generality for SNV detection. In the current design, there (c.35G>T), KRAS G13C (c.37G>T), and KRAS G13V is no need to change the capture sequence on the magnetic (c.38G>T), 0.05−0.1% abundances were also identified (Figbeads for the same kind of gene. The design can be further ure 3B). For 2 RNA mutations, KRAS G12V (c.35G>U) and generalized to reduce the cost in oligonucleotide-synthesis and KRAS G12D (c.35G>A) in the codon 12, 0.1% abundance was functionalization of FNPs for different gene mutation detecachieved (Figure 3C). The above results suggested that the tions if needed. As shown in Figure 5A, the capture-DNAproposed method hold the great potential in screening of mulfunctionalized magnetic beads and the signaling-DNAtiplex SNVs at very low abundance. functionalized FNPs are decoupled from the SNV, thus the To demonstrate the adaptability for practical applications, two types of DNA functionalized nanoparticles can be used we applied the proposed method to human genomic DNA for any sandwich designs to target different gene sequences. SNV detection of the cell sample (Figure 4A). First, we veriAlthough the DF value was reduced from 228 to 180 for KRAS fied the feasibility for PCR amplicon of KRAS G12D G12D (c.35G>A) (Figure 5B), the 0.05% low-abundance (c.35G>A) by using the synthetic sample. Specifically, the SNV detectability was not compromised and still significantly SNV target and WT were mixed at 100:0, 1:99, 0.5:99.5,


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Analytical Chemistry higher than that by spectrofluorometry (Figure 5C and 5D). This shed lights on developing the sandwich assay for multiple SNV detection from different genes with marvelously reduced cost. CONCLUSION In summary, we successfully realized the ultra-specific and sensitive multiplex single-nucleotide variant detection by combining the masking tactic with the FNP counting detection mode based on the sandwich assay. The highlights of this work demonstrated that the proposed method possesses a rivaling performance on both the discrimination factor and the low-abundance detectability. The detection of low-abundance mutation from human genomic DNA following PCR suggests the great potential for practical molecular diagnostic applications. This work expands the use of competition-mechanismbased strategy and the sensitive multiplexed detectability of FNP counting in SNV study with the sandwich assay as a vehicle. ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Oligonucleotide sequences, instrumentation, thermodynamic penalty calculation, supporting data, and summary of literature methods.

AUTHOR INFORMATION Corresponding Author ORCID of Corresponding Authors Na Li: 0000-0002-1496-8798 Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (No. 21535006 and 21475004). The authors are grateful to Prof. Dr. Zhi Zheng at Peking Union Medical College for providing cell samples.

REFERENCES (1) Chen, X.; Sullivan, P. F. Pharm. J. 2003, 3, 77-96. (2) Kim, S.; Misra, A. Annu. Rev. Biomed. Eng. 2007, 9, 289-320. (3) Kolpashchikov, D. M. Chem. Rev. 2010, 110, 4709-4723. (4) Khanna, M.; Park, P.; Zirvi, M.; Cao, W. G.; Picon, A.; Day, J.; Paty, P.; Barany, F. Oncogene 1999, 18, 27-38. (5) Linardou, H.; Briasoulis, E.; Dahabreh, I. J.; Mountzios, G.; Papadimitriou, C.; Papadopoulos, S.; Bafaloukos, D.; Kosmidis, P.; Murray, S. Cancer Treat. Rev. 2011, 37, 221-233. (6) Macedo, M. P.; Andrade, L. D. B.; Coudry, R.; Crespo, R.; Gomes, M.; Garcia Lisboa, B. C.; Aguiar, S., Jr.; Soares, F. A.; Carraro, D. M.; Cunha, I. W. Int. J. Colorectal. Dis. 2011, 26, 1241-1248. (7) Yeh, H.-C.; Sharma, J.; Shih, I.-M.; Vu, D. M.; Martinez, J. S.; Werner, J. H. J. Am. Chem. Soc. 2012, 134, 11550-11558. (8) Dijkstra, J. R.; Heideman, D. A. M.; Meijer, G. A.; Boers, J. E.; t Hart, N. A.; Diebold, J.; Hirschmann, A.; Hoefler, G.; Winter, G.; MiltenbergerMiltenyi, G.; Pereira, S. V.; Richman, S. D.; Quirke, P.; Rouleau, E. L.; Guinebretiere, J. M.; Tejpar, S.; Biesmans, B.; van Krieken, J. H. J. M. Virchows Archiv. 2013, 462, 39-46. (9) Herreros-Villanueva, M.; Chen, C.-C.; Yuan, S.-S. F.; Liu, T.-C.; Er, T.-K. Clinica Chimica Acta 2014, 431, 211-220. (10) Bournet, B.; Buscail, C.; Muscari, F.; Cordelier, P.; Buscail, L. Eur. J. Cancer 2016, 54, 75-83. (11) Altshuler, D.; Durbin, R. M.; Abecasis, G. R.; Bentley, D. R.; Chakravarti, A.; Clark, A. G.; Collins, F. S.; De la Vega, F. M.; Donnelly,

P.; Egholm, M.; Flicek, P.; Gabriel, S. B.; Gibbs, R. A.; Knoppers, B. M.; Lander, E. S.; Lehrach, H.; Mardis, E. R.; McVean, G. A.; Nickerson, D.; Peltonen, L., et al. Nature 2010, 467, 1061-1073. (12) DePristo, M. A.; Banks, E.; Poplin, R.; Garimella, K. V.; Maguire, J. R.; Hartl, C.; Philippakis, A. A.; del Angel, G.; Rivas, M. A.; Hanna, M.; McKenna, A.; Fennell, T. J.; Kernytsky, A. M.; Sivachenko, A. Y.; Cibulskis, K.; Gabriel, S. B.; Altshuler, D.; Daly, M. J. Nat. Genet. 2011, 43, 491-498. (13) Taly, V.; Pekin, D.; Benhaim, L.; Kotsopoulos, S. K.; Le Corre, D.; Li, X.; Atochin, I.; Link, D. R.; Griffiths, A. D.; Pallier, K.; Blons, H.; Bouche, O.; Landi, B.; Hutchison, J. B.; Laurent-Puig, P. Clin. Chem. 2013, 59, 1722-1731. (14) Hamada, M.; Shimase, K.; Tsukagoshi, K.; Hashimoto, M. Electrophoresis 2014, 35, 1204-1210. (15) Hashimoto, M.; Barany, F.; Xu, F.; Soper, S. A. Analyst 2007, 132, 913-921. (16) Inoue, A.; Han, A.; Makino, K.; Hosokawa, K.; Maeda, M. Lab Chip 2009, 9, 3297-3302. (17) Algar, W. R.; Krull, U. J. Anal. Chem. 2009, 81, 4113-4120. (18) Gong, Z. L.; Teixeira, C.; Xing, J. Z.; Yokota, J.; Kohno, T.; Gabos, S.; Le, X. C.; Li, X. F. Clin Chem. 2004, 50, 1441-1444. (19) Raz, N.; Danin-Poleg, Y.; Hayman, R. B.; Bar-On, Y.; Linetsky, A.; Shmoish, M.; Sanjuan, E.; Amaro, C.; Walt, D. R.; Kashi, Y. Plos One 2014, 9, e114576. (20) Xu, J.; Li, L.; Chen, N.; She, Y.; Wang, S.; Liu, N.; Xiao, X. Chem. Commun. 2017, 53, 9422-9425. (21) Wang, J.; Xiong, G.; Ma, L.; Wang, S.; Zhou, X.; Wang, L.; Xiao, L.; Su, X.; Yu, C. Biosens.Bioelectron. 2017, 94, 305-311. (22) Li, L.; Xiao, X.; Ge, J.; Han, M.; Zhou, X.; Wang, L.; Su, X.; Yu, C. ACS Sens. 2017, 2, 419-425. (23) Yu, Y.; Wu, T.; Johnson-Buck, A.; Li, L.; Su, X. Biosens.Bioelectron. 2016, 82, 248-254. (24) Wu, T.; Xiao, X.; Gu, F.; Zhao, M. Chem. Commun. 2015, 51, 17402-17405. (25) Huang, S.; Hu, J.; Zhu, G.; Zhang, C. Biosens.Bioelectron. 2015, 65, 191-197. (26) Wu, L. R.; Chen, S. X.; Wu, Y.; Patel, A. A.; Zhang, D. Y. Nat. Biomed. Eng. 2017, 1, 714-723. (27) Cheng, Y.; Du, Q.; Wang, L.; Jia, H.; Li, Z. Anal. Chem. 2012, 84, 3739-3744. (28) Patolsky, F.; Lichtenstein, A.; Willner, I. Nat. Biotechnol. 2001, 19, 253-257. (29) Sun, Y.; Sun, Y.; Tian, W.; Liu, C.; Gao, K.; Li, Z. Chem. Sci. 2018, 9, 1344-1351. (30) Wang, H.; Wang, H.; Duan, X.; Sun, Y.; Wang, X.; Li, Z. Chem. Sci. 2017, 8, 3635-3640. (31) Hu, S.; Tang, W.; Zhao, Y.; Li, N.; Liu, F. Chem. Sci. 2017, 8, 10211026. (32) Stancescu, M.; Fedotova, T. A.; Hooyberghs, J.; Balaeff, A.; Kolpashchikov, D. M. J. Am. Chem. Soc. 2016, 138, 13465-13468. (33) Das, J.; Ivanov, I.; Sargent, E. H.; Kelley, S. O. J. Am. Chem. Soc. 2016, 138, 11009-11016. (34) Wang, J. S.; Zhang, D. Y. Nature Chem. 2015, 7, 545-553. (35) Miotke, L.; Maity, A.; Ji, H.; Brewer, J.; Astakhova, K. Plos One 2015, 10, e0136720. (36) Das, J.; Ivanov, I.; Montermini, L.; Rak, J.; Sargent, E. H.; Kelley, S. O. Nature Chem. 2015, 7, 569-575. (37) Dose, C.; Ficht, S.; Seitz, O. Angew. Chem. Int. Ed. 2006, 45, 53695373. (38) Huang, J.; Wang, Z.; Kim, J.-K.; Su, X.; Li, Z. Anal. Chem. 2015, 87, 12254-12261. (39) Li, P.; He, H.; Wang, Z.; Feng, M.; Jin, H.; Wu, Y.; Zhang, L.; Zhang, L.; Tang, X. Anal. Chem. 2016, 88, 883-889. (40) Liu, J.; Lu, Y.; Feng, L.; Wang, S.; Zhang, S.; Zhu, X.; Sheng, L.; Zhang, S.; Zhang, X. Anal. Chem. 2017, 89, 2622-2627. (41) Noor, M. O.; Krull, U. J. Anal. Chem. 2014, 86, 10331-10339. (42) Ihara, T.; Uemura, A.; Futamura, A.; Shimizu, M.; Baba, N.; Nishizawa, S.; Teramae, N.; Jyo, A. J. Am. Chem. Soc. 2009, 131, 13861387. (43) Xi, D.; Shang, J.; Fang, E.; You, J.; Zhang, S.; Wang, H. Anal. Chem. 2016, 88, 10540-10546. (44) Lee, K.; Cui, Y.; Lee, L. P.; Irudayaraj, J. Nat. Nanotech. 2014, 9, 474-480. (45) Ma, F.; Li, Y.; Tang, B.; Zhang, C. Acc. Chem. Res. 2016, 49, 17221730.



(46) Hu, J.; Wang, Z.; Li, C.; Zhang, C. Chem. Commun. 2017, 53, 1328413295. (47) Hu, J.; Liu, M.; Li, Y.; Tang, B.; Zhang, C. Chem. Sci. 2018, 9, 712720. (48) Wang, Z.; Wang, L.; Zhang, Q.; Tang, B.; Zhang, C. Chem. Sci. 2018, 9, 1330-1338. (49) Tost, J.; Gut, I. G. Clin. Biochem. 2005, 38, 335-350. (50) Haff, L. A.; Smirnov, I. P. Genome Res. 1997, 7, 378-388. (51) Xue, X.; Xu, W.; Wang, F.; Liu, X. J. Am. Chem. Soc. 2009, 131, 11668-11669. (52) Wu, F.; Chen, M.; Lana, J.; Xia, Y.; Liu, M.; He, W.; Li, C.; Chen, X.; Chen, J. Sensor Actuat. B-chem 2017, 241, 123-128. (53) Hamidi-Asl, E.; Raoof, J. B.; Hejazi, M. S.; Sharifi, S.; Golabi, S. M.; Palchetti, I.; Mascini, M. Electroanalysis 2015, 27, 1378-1386. (54) Li, Y.-Q.; Guan, L.-Y.; Wang, J.-H.; Zhang, H.-L.; Chen, J.; Lin, S.; Chen, W.; Zhao, Y.-D. Biosens.Bioelectron. 2011, 26, 2317-2322. (55) Chen, F.; Zhao, Y.; Fan, C.; Zhao, Y. Anal. Chem. 2015, 87, 87188723. (56) Xiao, Y.; Plakos, K. J. I.; Lou, X.; White, R. J.; Qian, J.; Plaxco, K. W.; Soh, H. T. Angew. Chem. Int. Ed. 2009, 48, 4354-4358. (57) Sayyed, D. R.; Nimse, S. B.; Song, K.-S.; Kim, T. Chem. Commun. 2014, 50, 12344-12347.

(58) Tyagi, S.; Bratu, D. P.; Kramer, F. R. Nat. Biotechnol. 1998, 16, 4953. (59) Lin, Y.-W.; Ho, H.-T.; Huang, C.-C.; Chang, H.-T. Nucleic Acids. Res. 2008, 36, e123. (60) Pei, X.; Yin, H.; Lai, T.; Zhang, J.; Liu, F.; Xu, X.; Li, N. Anal. Chem. 2018, 90, 1376-1383. (61) Shen, J.; Li, Y.; Gu, H.; Xia, F.; Zuo, X. Chemical Reviews 2014, 114, 7631-7677. (62) Wang, Z.; Fan, Y.; Chen, J.; Guo, Y.; Wu, W.; He, Y.; Xu, L.; Fu, F. Electrophoresis 2013, 34, 2177-2184. (63) Morla-Folch, J.; Gisbert-Quilis, P.; Masetti, M.; Garcia-Rico, E.; Alvarez-Puebla, R. A.; Guerrini, L. Angew. Chem. Int. Ed. 2017, 56, 2381-2385. (64) Li, T.; Zheng, Y.; Sun, H.; Zhuang, R.; Liu, J.; Liu, T.; Cai, W. Med. Oncol. 2016, 33, 61. (65) Zhang, H.; Song, J.; Ren, H.; Xu, Z.; Wang, X.; Shan, L.; Fang, J. Plos One 2013, 8, e54510.


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