{"paper_id":"4cc843d6-cbab-4bd9-b7a3-e1f4cb909068","body_text":"Construction of scalable multi-channel DNA nanoplatform for the combined detection of multi-component biomarkers of cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction of scalable multi-channel DNA nanoplatform for the combined detection of multi-component biomarkers of cancer Yiwei Song, Xiuyan Jin, Yiou zhao, Shuwen Cheng, Sai Xu, Shengjun Bu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4530662/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2024 Read the published version in Microchimica Acta → Version 1 posted 9 You are reading this latest preprint version Abstract Single-level biomarker detection has the limitation of insufficient accuracy in cancer diagnosis. Therefore, the strategy of developing highly sensitive, multi-channel biosensors for multi-component biomarkers analysis is critical to improve the accuracy of early diagnosis of clinical tumors. Herein, in order to achieve efficient detection of up to ten targets for early diagnosis of ovarian cancer, a DNA-nanoswitch-based multi-channel (DNA-NSMC) biosensor was built based on the multi-module catalytic hairpin assembly-mediated signal amplification (CHA) and toehold-mediated DNA strand displacement (TDSD) reaction. In this work, only two different fluorescence signals are used as outputs, combined with modular segmentation strategy of DNA-nanoswitch-based reaction platform, the multi-channel detection of up to 10 targets is successfully achieved for the first time. The experimental results suggest that the proposed biosensor is a promising tool for simultaneously detecting multiple biomarkers for the early diagnosis of ovarian cancer, offering new strategies for the early screening, diagnosis, and treatment not only for ovarian cancer but also for other cancers. Ovarian cancer DNA nanoswitch Multi-channel sensing CHA ctDNA detection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Early detection of cancer-related biomarkers is a perennial topic in the fields of molecular biology and clinical diagnostics. According to the Cancer Statistics Report 2023 [ 1 ], the mortality rate associated with ovarian cancer is as high as 67.3%, making it one of the top 10 cancers worldwide. More than 60% of patients are diagnosed with advanced stage III or later, with a 5-year survival rate of about 46% [ 2 ]. However, if diagnosed at stage I, the 5-year survival rate can be as high as 92% [ 3 ]. Currently, 80% of ovarian cancer patients are diagnosed in advanced stages due to their symptoms are vague and are often thought by women to be related to aging, menopause and early pregnancy [ 4 ]. Therefore, the early diagnosis and treatment of ovarian cancer are facing serious challenges. Clinically, the protein biomarker Carbohydrate Antigen 125 (CA125) in the blood is utilized as a reliable biomarker for detecting, diagnosing, and tracking ovarian tumor/cancer recurrence [ 5 – 7 ]. However, the accuracy of detecting ovarian cancer using CA125 remains low [ 8 , 9 ]. Although combined detection methods have been developed, including the concurrent detection of proteins such as CA125 and Human epididymis protein 4 (HE4), their specificity and sensitivity are still difficult to meet the needs [ 10 – 13 ]. In recent years, continuous studies have shown that detection methods targeting miRNA and Circulating-tumor DNA (ctDNA) have greatly improved the accuracy of early cancer diagnosis [ 14 – 16 ]. Y. Sun et al. designed a novel fluorescence sensor based on E36 encapsulated vesicles to detect miRNA-21 through the specific interaction between E36 and miRNA-21 [ 17 ]. By modifying magnetic microspheres with gold nanoparticles, C. Li et al. constructed a novel nanoMBs-based biosensor for ctDNA detection, which not only showed good stability, but also realized the detect limitation of 0.1 nM with the detection range of 0.2–20 nM [ 18 ]. Although the sensitivity related to the early diagnosis of ovarian cancer has been greatly improved by using RNA and ctDNA as targets, its specificity and sensitivity need to be further improved. Most of the existing studies only focus on the detection of ovarian cancer with no more than three targets [ 6 ], lacking of efficient multi-channel and multi-component detection methods to further improve the sensitivity and selectivity of the targets related to the early diagnosis of ovarian cancer. With the development of DNA nanostructures, DNA is no longer simply considered as the carrier of genetic information. Due to its unique base-pairing properties, DNA has become the structural unit of constructing nanomaterials [ 19 ], playing a significant role in the high-sensitivity detection of nucleic acids, proteins, viruses, bacteria, and other targets. Especially in the detection of nucleic acid-based targets, which shows a promising prospect for the early diagnosis and treatment of cancer. J. Li et al. developed a Cayley tree-like fractal DNA framework with topological encoding of fluorescence states for multiplex detection of low-abundance targets [ 20 ]. Such structure allows modular design of DNA nanostructures with tunable mechanical properties, providing a highly versatile toolkit for multiplexing and quantitative detection of low-abundance biological targets. W. Diao et al. developed a surface plasmon resonance (SPR) biosensing strategy based on entropy-driven strand displacement reaction (ESDR) and double-layer DNA tetrahedra (DDT) for highly sensitive detection of HIV-related DNA [ 21 ]. In addition, the application of DNA nanostructure for the construction of fluorescent switching system has also shown good sensitivity and specificity for the early diagnosis of cancer. X. Y. Yang et al. built a spherical recognition probe and toe-mediated strand displacement reaction-induced silver nanocluster (AgNCs) fluorescence signal switch system for the ct DNA detection related to Alzheimer's disease. 22 Due to the advantages of programmability, biocompatibility and biodegradability, DNA nanostructures have shown potential prospects for application in biological detection, particularly in the construction of multi-channel nano-reaction platform and the detection of multi-component nucleic acid targets. At present, biosensors with fluorescence, electrochemical, Raman and colorimetry as signal outputs have been constructed for the detection of early cancer biomarkers. Among them, fluorescence signal has been widely used because of its simplicity, high specificity and high sensitivity in ultramicroscopic samples. 23,24 However, detection methods based on the fluorescence signal as an output are still limited by the ability to detect multiple types of targets in parallel, due to the limitations of the types of fluorescence dye that can be modified into nucleic acids for commercialization. Therefore, in order to overcome the above problems and achieve the multi-channel detection of up to ten ctDNA targets related to the early diagnosis of ovarian cancer in this work, firstly, a DNA-nanoswitch-based multi-channel (DNA-NSMC) platform was constructed. By modularizing the DNA-NSMC platform, high sensitivity detection of up to ten targets is realized by using only two fluorescence signals as outputs. In addition, this method can simultaneously segment the fluorescence output signal in multiple channels, effectively avoiding the difficulty of increasing the types of detection targets due to the limitation of the types of commercial fluorophores. Not only that, it can also effectively avoid the situation of signal crosstalk caused by excessive fluorophores. Secondly, in order to further improve the sensitivity, the enzyme-free catalytic hairpin assembly-mediated signal amplification (CHA) was introduced, which was also modularized. It is worth noting that the modular segmentation strategy proposed in this work is scalable. With the increase of detection target types, it is expected that the number of detection targets can be extended up to 2 n by increasing the number of platform subunits to 2 n . Based on the above strategies, in this work, we successfully realized the fluorescence detection of ten ctDNA targets associated with early diagnosis of ovarian cancer for the first time, and showed good sensitivity and specificity. In addition, it also showed stable detection characteristics in actual human serum, which provided a new idea for the early diagnosis and treatment of ovarian cancer. Experimental section Chemicals and Instruments The DNAs utilized in this study were synthesized by Hippo Biotechnology Company (Zhejiang, China) and the sequences are listed in Table S1 -S3 (Supporting Information). All DNA samples were dissolved in ultrapure water and quantified using the Thermo Nanodrop One. Chemicals including acrylamide, methylene bisacrylamide, ammonium persulfate, TEMED, Boric acid and EDTA were procured from Aladdin Biochemical Technology Co. LTD. All reagents were of analytical grade without further purification. All the solutions used in this work were prepared by ultrapure water (> 18 MΩ) obtained from two-stage reverse osmosis purification system. Prior to application, the DNAs were thinned with TE buffer (10×10 − 3 M Tris-base, 1×10 − 3 M EDTA, pH = 8.0) before use. The native polyacrylamide gel electrophoresis experiments were conducted using an electrophoresis tank from BeiJing Jun Yi. Gel images were captured using Bio-Rad. The fluorescence emission spectra were collected utilizing the FL6500 fluorescence spectrometer . Measurement of Fluorescence The fluorescence emission spectra of the output were recorded in TE buffer at room temperature. The excitation wavelength of FAM and HEX are 495 and 535 nm, and emission spectra were measured from 510 to 550 nm and from 540 to 575 nm. The slit widths are set to 5 nm for both excitation and emission. Native Polyacrylamide Gel Electrophoresis (PAGE) Prior to usage, the DNA solutions were heated at 90°C for 10 min and gradually cooled to room temperature. Subsequently, a mixture of platforms and targets were added to reach a final volume of 100 µL and then incubated for 60 min. The 12% PAGE was prepared, and electrophoresis was carried out in 1×TBE buffer (89×10 − 3 M Tris-boric, 2×10 − 3 M EDTA, pH = 8.0) at a constant voltage of 100 V for about 60 min. Gels imaging using a gel image system (Bio-Rad Laboratories). Operation of detecting target The multi-channel platform based on DNA-nanoswitch (P 1 − 1 -P 5 − 2 ) that Five centrifuge tubes with equal distribution were utilized as the universal system for the detection of 10 biomarkers of ovarian cancer. Different DNA strands (excluding the target sequences and hairpin structures) solutions were heated to approximately 90°C for 10 minutes and then allowed to cool gradually to room temperature. In accordance with the reaction requirements, the sequences of p-DNA, f-DNA, q-DNA used for constructing platforms were pre-mixed and subsequently added in equal proportions to five centrifuge tubes containing the reaction platform. Following approximately an hour of reaction time, the target sequences were introduced into all test solutions. After the completion of the reactions, the contents from the centrifuge tubes were carefully transferred to a cuvette. Following this transfer, the output signals were swiftly and efficiently tested using a fluorescence spectrophotometer. The optimized concentrations of target and DNA-nanoswitch-based platform were combined to achieve a final volume of 100 µL. Signal amplification design Different DNA strands (excluding the target sequences and hairpin structures) solutions were heated to approximately 90°C for 10 min and gradually cooled to room temperature. In accordance with the reaction requirements, the DNA sequences utilized in constructing platforms were pre-mixed and then added in equal amounts to five centrifuge tubes. Next, the hairpin-structure DNAs involved in the amplification reaction were added to the multi-channel mixtures. When the targets were added and the reactions were complete, the reactants in the centrifuge tube were carefully transferred to the cuvette and the output signals were quickly tested using a fluorescence spectrometer. The optimized concentrations of the target and DNA nanoswitch-based platform were combined to reach a final volume of 100 µL. Results and discussion The design of biosensor In this work, in order to achieve multi-channel and enzyme-free detection of up to ten targets related to the early diagnosis of ovarian cancer, a DNA-based fluorescence switch set was first constructed and used as the reaction platform for detection, as shown in Fig. 1 . In order to overcome the problem that the detection scale is limited due to the difficulty in obtaining ten different kinds of fluorescent dyes with varying emission peaks as output signal markers, the following strategies were adopted in this work to construct an ideal reaction platform for target detection. First, a series of ten monomers based on DNA-nanostructure (P 1 − 1 , P 1 − 2 , … P 5 − 1 , P 5 − 2 ) as the reaction platform was constructed, as shown in Fig. 1 a. Each monomer consisted of three DNA single strands, with two strands modified by fluorophore and quenching group, which hybridized with the third single strand. All of the monomers contain only two different fluorescent dyes as modifications. Second, the ten reactive monomers were modularly segmented, and each two switch structures with different fluorescent modifications (FAM and HEX) were used as a subset. Therefore, such 10 reaction monomers were evenly divided into five modules, which were placed in five centrifuge tubes, as shown in Fig. 1 b. Accordingly, the ten output signals were partitioned into five separate subsets too. Third, to enhance the sensitivity of the detection system, the amplification reaction based on CHA was introduced in this work, as shown in Fig. 1 c. Among them, ten corresponding amplification systems were designed for the detection of ten targets, and modular segmentation was also carried out. By integrating with the loading reaction platform of a cuvette with signal acquisition using fluorescence spectrophotometer, the multi-channel and distinguishable detection of the ten output signals labeled with only two fluorescent dyes is realized. The reaction system showed excellent scalability, allowing for direct expansion of the multi-channel reaction platform by increasing the number of reaction monomers, and then the scale of biological detection can be increased. This strategy shows potential research and application value, which motivates us to continue to explore in the follow-up work to achieve breakthroughs in large-scale detection. Construction validation of the DNA-NSMC platform The reaction mechanism of ten monomers (P 1 − 1 , P 1 − 2 , … P 5 − 1 , P 5 − 2 ) in the DNA-NSMC-based platform is shown in Fig. 2 a. The optimized concentrations of three single-stranded DNAs comprising each DNA-nanostructure-based monomer has been discussed in Fig. S1 and Fig. S2. The toehold-mediated DNA strand displacement (TDST) reaction can occur by introducing one or more targets, facilitating the restoration of fluorescence signals that have been quenched on the reaction platform. Therefore, by monitoring the “on” and “off” of the fluorescence signal, as well as regulating its intermediate state process, the target can be successfully sensed. As shown in Fig. 2 b, the background signal intensity of the DNA-NSMC platform before adding the target (column 1) and the fluorescence recovery intensity after adding the target (column 2) are shown, indicating the successful construction of ten reaction monomers based on the DNA-nanoswitch structure. Furthermore, we further proved that even when ten monomers were divided into five subsets, each subset maintained an optimal fluorescence switching effect and exhibited minimal background signal (Fig. S3). Furthermore, the structural integrity of the DNA-NSMC platform composed of ten reaction monomers was further demonstrated by native polyacrylamide gel electrophoresis (PAGE) experiments, as shown in Fig. 2 c. All the DNA sequences involved in the construction of DNA-NSMC platform have been determined by different belts. Using the formation of the P 1 − 1 monomer as an example, from Lane-1 to Lane-3, the bands showed the single-stranded p 1 − 1 , f 1 − 1 , and q 1 − 1 . The locations of q n−1 were not obvious, which may be because their molecular weights are too small to stay in the gel. When f 1 − 1 was added to the p 1 − 1 , a new band appeared in Lane-4, indicating the hybridization happened and formed the duplex of p 1 − 1 /f 1 − 1 . In a similar way, When the q 1 − 1 was added to the p 1 − 1 , a new band appeared in Lane-5, indicating the hybridization happened and formed the duplex of p 1 − 1 /q 1 − 1 . However, when the three components were mixed together, a new band emerged in Lane-6, indicating the hybridization happened and formed the duplex of p 1 − 1 /(f 1 − 1 +q 1 − 1 ), which is the reaction monomer of P 1 − 1 . Validation of CHA reaction As shown in Fig. 3 , which has been successfully proved that the introduction of CHA into the reaction system based on DNA-NSMC platform can effectively improve the sensitivity of detection. When there was no target in each subset, the mixture of p-DNA/f-DNA/q-DNA (column 1, Fig. 3 ) exhibited low fluorescence intensity, which was attributed to effective control of background in platform and Fluorescence resonance energy transfer (FRET) reaction. When the target was added to the reaction platform without CHA, it can undergo TDST reaction with the platform, leading to the restoration of the fluorescence signal (column 2, Fig. 3 ). However, when the addition of target into the reaction platform with CHA, the hybridization product between the target and the platform will further activate the hairpin sequence of H n associated with the CHA, as shown in Fig. 1 c, thus triggering the amplification reaction and leading to further enhancement of the fluorescence signal (column 3, Fig. 3 ). All H-DNA sequences involved in the CHA are listed in Table S2. Comparing with the target detection without CHA reaction, an about 500% fluorescence signal enhancement is observed, which showed up to twice the signal enhancement. Target detection and selectivity based on DNA-NSMC platform By analyzing the dependence of fluorescence signal response with different concentrations of target ctDNA, the sensitivity of this work was evaluated. As shown in Fig. 4 a-d, we take the detection of T 5 − 1 and T 5 − 2 without (Fig. 4 a and c) and with (Fig. 4 b and d) CHA participation as an example, the corresponding fluorescence intensity gradually increased when the concentration of target ctDNA was raised from 100 to 500 nM. On DNA-nanoswitch-based platform without CHA (Fig. 4 e, f, black correction curve), the fluorescence intensity at 517 nm and 556 nm varies linearly with the increase of T 5 − 1 and T 5 − 2 concentrations, and the detection limits (LOD) were 45.51 nM and 29.74 nM (3σ/S, where σ represents the standard deviation of the background signal, and S represents the slope of linear curve), respectively. However, on DNA-nanoswitch-based platform containing CHA (Fig. 4 e, f, red correction curve), the fluorescence intensity at 517 nm and 556 nm showed a steeper linear change with the increase of T 5 − 1 and T 5 − 2 concentrations, and the LOD were 1.45 nM and 1.13 nM, respectively, resulting in a 31.5-fold and 26.3-fold lower than that of the detection system without CHA (Fig. 4 e, f). Other DNA-nanoswitch-based reaction subset and their detection characteristics were explained in detail in Fig. S4-S7. Therefore, the proposed DNA-NSMC biosensor based on the multi-module CHA can not only realize multi-component detection under multi-channel, but also show better sensitivity. To assess the specificity of the DNA-based multi-channel biosensor. Four mismatched DNA sequences were chosen as control groups, and the corresponding sequences were listed in Table S3. As shown in Fig. 4 g, h, compared with single-mismatched, three-mismatched, five-mismatched target genes and miRNA-21, the detection system constructed in this work showed a lower response, indicating good detection specificity. Application to detect target in human serum As shown in Fig. 5 , the practical application of the DNA-based multi-channel biosensor was conducted using human serum spiked with target gene at various concentrations, taking the detection of T 5 − 1 and T 5 − 2 as an example. In this work, different concentrations of target genes were added to 5% and 10% of human serum, respectively, and the analysis results were shown in Fig. 5 a, b. As shown in Table 1 , the spiked recovery in T 5 − 1 and T 5 − 2 detection ranged from 86–105%, with a relative standard deviation (RSD) below 9.78% in all groups. These results indicate that our prepared DNA biosensor exhibits robust anti-interference capability in complex matrix. Table 1 Real sample assays using human serum for T 5 − 1 and T 5 − 2 gene detection (n = 3) Samples T 5 − 1 gene (nM) Assay results (nM) Recovery (%) RSD (%) 5% serum 10% serum 5% serum 10% serum 5% serum 10% serum 1 100 98.38 91.846 98.38% 91.85% 5.89% 2.41% 2 200 199.57 202.38 99.79% 101.19% 3.59% 1.21% 3 300 299.74 301.64 99.91% 100.55% 2.70% 1.51% 4 400 405.90 402.21 101.48% 100.55% 3.09% 1.52% 5 500 502.10 498.09 100.42% 99.62% 1.34% 1.15% Samples T 5 − 2 gene (nM) Assay results (nM) Recovery (%) RSD (%) Samples T 5 − 1 gene (nM) Assay results (nM) 5% serum 10% serum 5% serum 10% serum 5% serum 10% serum 1 100 101.29 100.16 101.29% 100.164% 9.53% 2.56% 2 200 194.04 195.61 97.02% 97.81% 4.56% 9.59% 3 300 299.50 300.12 99.83% 100.04% 2.78% 1.88% 4 400 402.58 399.52 100.65% 99.88% 1.99% 1.37% 5 500 503.15 496.82 100.63% 99.36% 1.37% 1.45% Conclusions In summary, in order to improve the accuracy of early diagnosis of ovary cancer and overcome the difficulty of improving detection sensitivity and specificity due to the limited types of detection targets, in this study, established a DNA-nanoswitch-based multi-channel biosensor utilizing a multi-module catalytic hairpin assembly-mediated signal amplification system. With this strategy, we have successfully achieved fluorescence detection of up to ten ctDNA targets associated with the early diagnosis of ovarian cancer. For large-scale detection targets, including the detection strategy in this study, which involves responding to ten different targets, typically necessitates the use of 10 fluorescent dyes with distinct emission peaks as output signal markers for detection and recognition.However, due to current limitations in the development of science and technology, it is challenging to identify 10 suitable fluorescent dyes. This limitation also represents one of the difficulties encountered in expanding the scale of biological detection. To address this issue, we have adopted the following strategies: (1) a DNA-based fluorescence switch set, including ten DNA–nanoswitch monomers (P 1 − 1 , P 1 − 2 , … P 5 − 1 , P 5 − 2 ), was first constructed and used as the reaction platform for detection; (2) The ten reactive monomers were modularized and evenly divided into five subsets, which were positioned in turn in five holes of the 96-well plate; (3) In order to further improve the sensitivity of the detection system, 10 CHA-based amplification reactions corresponding to each channel are introduced in this work, which are also modularized. In addition, the reaction system demonstrates promising scalability, and the scale of multi-channel reaction platform can be directly expanded by increasing the number of reaction monomers, thus improving the scale of biological detection. The experimental findings suggest that the proposed biosensor holds promise as a tool for the simultaneous detection of multiple biomarkers in ovarian cancer. We commit that in the future, leveraging the design concept of this study, which harnesses the potent DNA chain displacement reaction and CHA, we will continue to explore further to accomplish even larger and more intricate biological assays. Declarations Author contribution YiWei Song: Writing - original draft, Validation, Methodology, Investigation. Xiuyan Jin:Investigation, Data curation. Shengjun Bu:Supervision, Project. Liming Liu: administration, Conceptualization. Chunyang Zhou: Project administration, Investigation, Writing - original draft and lead. Chunying Pang: Investigation, Supporting. Funding This work were supported by Jilin Province Science and Technology Department projects (YDZJ202301ZYTS33, 20220204127YY and 20240101203JC) and National Natural Science Foundation of China (52071048). 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Supplementary Files SI20240603.docx Cite Share Download PDF Status: Published Journal Publication published 21 Aug, 2024 Read the published version in Microchimica Acta → Version 1 posted Editorial decision: Revision requested 18 Jul, 2024 Reviews received at journal 15 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 10 Jun, 2024 Submission checks completed at journal 10 Jun, 2024 First submitted to journal 04 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4530662\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":317789292,\"identity\":\"9ef7c9e5-d448-4b8c-87ec-5d4552ff3f29\",\"order_by\":0,\"name\":\"Yiwei Song\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiwei\",\"middleName\":\"\",\"lastName\":\"Song\",\"suffix\":\"\"},{\"id\":317789296,\"identity\":\"2ffe286c-745b-40c7-bbfc-98398eccf2f8\",\"order_by\":1,\"name\":\"Xiuyan Jin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiuyan\",\"middleName\":\"\",\"lastName\":\"Jin\",\"suffix\":\"\"},{\"id\":317789301,\"identity\":\"5caf0d79-a103-4efc-b646-8b783b3383e6\",\"order_by\":2,\"name\":\"Yiou zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiou\",\"middleName\":\"\",\"lastName\":\"zhao\",\"suffix\":\"\"},{\"id\":317789302,\"identity\":\"4be0c04f-dbcc-498a-bfe3-4d139a1a4b13\",\"order_by\":3,\"name\":\"Shuwen Cheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shuwen\",\"middleName\":\"\",\"lastName\":\"Cheng\",\"suffix\":\"\"},{\"id\":317789306,\"identity\":\"7f3c4c86-c0bd-4cec-b59a-faa690f0b996\",\"order_by\":4,\"name\":\"Sai Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Dalian Maritime University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sai\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":317789310,\"identity\":\"f8907abf-bb00-43df-b148-a221a78b9f12\",\"order_by\":5,\"name\":\"Shengjun Bu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shengjun\",\"middleName\":\"\",\"lastName\":\"Bu\",\"suffix\":\"\"},{\"id\":317789313,\"identity\":\"e8586e53-5a61-4f52-9126-fccbe17e41a4\",\"order_by\":6,\"name\":\"Liming Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liming\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":317789316,\"identity\":\"5a740640-2be5-4150-a905-c56a259e330a\",\"order_by\":7,\"name\":\"Chunyang Zhou\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYHACNijNfODAhwqStLCxJR6ccYY0LTzGh3lbiFDPPyP92YOPbXaJ8+f3fDjA28Agzy92AL8WiRsJ6YYz25ITNxzj3XBAcgeD4czZCfi1GEgkHJPmbTuQuIENqMXwDEOCwW2CWhLbwFrmt/E8OJDYRpSWZDawloZjPAwHDhKjReLMMzbJGeeSjTccSzM42HBGgrBf+NvTn0l8KLOTnd98+PHnPxU28vzSBLQwCKAqkCCgHGzNASIUjYJRMApGwcgGAFZzR9GrOQ0HAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Chunyang\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":317789318,\"identity\":\"551e45c2-e8eb-4e84-a0e3-d8fe91467ce8\",\"order_by\":8,\"name\":\"Chunying Pang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Changchun University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chunying\",\"middleName\":\"\",\"lastName\":\"Pang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-05 01:31:28\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4530662/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4530662/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s00604-024-06632-6\",\"type\":\"published\",\"date\":\"2024-08-21T15:57:41+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":59020416,\"identity\":\"68b3ece5-a056-43ce-a2f6-2d75c15c00bb\",\"added_by\":\"auto\",\"created_at\":\"2024-06-25 11:45:54\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":422769,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDescription of the construction of the DNA-NSMC-based platform. Construction of DNA fluorescent switch set (\\u003cstrong\\u003ea\\u003c/strong\\u003e), modular segmentation (\\u003cstrong\\u003eb\\u003c/strong\\u003e), reaction mechanism and modular segmentation of CHA (\\u003cstrong\\u003ec\\u003c/strong\\u003e)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/1c7bc1723beaeca4f06083dc.png\"},{\"id\":59020413,\"identity\":\"b99a0e08-32ca-413c-b0e0-e8092b19e258\",\"added_by\":\"auto\",\"created_at\":\"2024-06-25 11:45:53\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":178348,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(\\u003cstrong\\u003ea\\u003c/strong\\u003e) Reaction mechanism of ten\\u003csub\\u003e \\u003c/sub\\u003emonomers in the DNA-NSMC platform. (\\u003cstrong\\u003eb\\u003c/strong\\u003e) Fluorescence intensity variations of the ten monomers before and after the addition of the target. (\\u003cstrong\\u003ec\\u003c/strong\\u003e) Implementation of PAGE experiment to demonstrate the structural integrity of ten reactive monomers\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/0203ae7e066c95b505b6a0e0.png\"},{\"id\":59020412,\"identity\":\"663b17e6-4231-4d18-bb44-34fc00227f51\",\"added_by\":\"auto\",\"created_at\":\"2024-06-25 11:45:53\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":25224,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFeasibility of the DNA biosensor based on CHA circuits. (\\u003cstrong\\u003ea-j\\u003c/strong\\u003e) Fluorescence analysis: Using the formation of P\\u003csub\\u003e1-1\\u003c/sub\\u003e monomer as an example, column 1, p\\u003csub\\u003e1-1\\u003c/sub\\u003e+ f\\u003csub\\u003e1-1\\u003c/sub\\u003e + q\\u003csub\\u003e1-1\\u003c/sub\\u003e (P\\u003csub\\u003e1-1\\u003c/sub\\u003e); column 2, T\\u003csub\\u003e1-1\\u003c/sub\\u003e + P\\u003csub\\u003e1-1\\u003c/sub\\u003e; column 3, H\\u003csub\\u003e1-1\\u003c/sub\\u003e + b\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/012d59aee3c0d185fdef9f37.png\"},{\"id\":59020849,\"identity\":\"875cf94b-cf12-4b33-b18c-ff9386a74930\",\"added_by\":\"auto\",\"created_at\":\"2024-06-25 11:53:54\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":239482,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePerformance evaluations of the biosensor based on CHA. Fluorescence profile corresponding to different concentrations of (\\u003cstrong\\u003ea-b\\u003c/strong\\u003e) T\\u003csub\\u003e5-1\\u003c/sub\\u003e gene and (\\u003cstrong\\u003ec-d\\u003c/strong\\u003e) T\\u003csub\\u003e5-2\\u003c/sub\\u003e gene (from top to bottom: 500 nM, 400 nM, 300 nM, 200 nM, 100 nM, 0 nM). (\\u003cstrong\\u003ee-f\\u003c/strong\\u003e) Calibration curves of response of different concentrations (0-500 nM) to target genes. Linear range was from 0 to 500 nM. (\\u003cstrong\\u003eg-h\\u003c/strong\\u003e) Specificity against different kinds of (\\u003cstrong\\u003eg\\u003c/strong\\u003e) T\\u003csub\\u003e5-1\\u003c/sub\\u003e and (\\u003cstrong\\u003eh\\u003c/strong\\u003e) T\\u003csub\\u003e5-2\\u003c/sub\\u003e gene at concentration of 500 nM: complementary Target, Single base mutation Target (OM), Three-base mutation Target (TM), Five base mutation Target (FM), miRNA-21, Blank control. The error bars were obtained via three independent experiments and denote standard deviation (S.D.)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/aa59ea98106900674ddc88fe.png\"},{\"id\":59020415,\"identity\":\"66405771-9ca5-414d-8e47-621840aa5eac\",\"added_by\":\"auto\",\"created_at\":\"2024-06-25 11:45:53\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":19133,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFluorescence assay results of the detection of (\\u003cstrong\\u003ea\\u003c/strong\\u003e) T\\u003csub\\u003e5-1\\u003c/sub\\u003e and (\\u003cstrong\\u003eb\\u003c/strong\\u003e) T\\u003csub\\u003e5-2\\u003c/sub\\u003e in real human serum solutions at 10% (orange), 5% (green) concentrations, and in PBS buffer (purple), respectively\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/9d475e259b621eec66e79958.png\"},{\"id\":63300677,\"identity\":\"89068486-9c16-4d9a-966a-af72b2c503a7\",\"added_by\":\"auto\",\"created_at\":\"2024-08-26 16:16:30\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1550799,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/c5a845a3-aa88-482b-a4cd-c7782768da74.pdf\"},{\"id\":59020850,\"identity\":\"c42e282c-a374-40b6-8539-e3005d1ec92f\",\"added_by\":\"auto\",\"created_at\":\"2024-06-25 11:53:54\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3825510,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SI20240603.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4530662/v1/e267b95b8712a66fe034520a.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Construction of scalable multi-channel DNA nanoplatform for the combined detection of multi-component biomarkers of cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEarly detection of cancer-related biomarkers is a perennial topic in the fields of molecular biology and clinical diagnostics. According to the Cancer Statistics Report 2023 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e], the mortality rate associated with ovarian cancer is as high as 67.3%, making it one of the top 10 cancers worldwide. More than 60% of patients are diagnosed with advanced stage III or later, with a 5-year survival rate of about 46% [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. However, if diagnosed at stage I, the 5-year survival rate can be as high as 92% [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Currently, 80% of ovarian cancer patients are diagnosed in advanced stages due to their symptoms are vague and are often thought by women to be related to aging, menopause and early pregnancy [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Therefore, the early diagnosis and treatment of ovarian cancer are facing serious challenges.\\u003c/p\\u003e \\u003cp\\u003eClinically, the protein biomarker Carbohydrate Antigen 125 (CA125) in the blood is utilized as a reliable biomarker for detecting, diagnosing, and tracking ovarian tumor/cancer recurrence [\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, the accuracy of detecting ovarian cancer using CA125 remains low [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Although combined detection methods have been developed, including the concurrent detection of proteins such as CA125 and Human epididymis protein 4 (HE4), their specificity and sensitivity are still difficult to meet the needs [\\u003cspan additionalcitationids=\\\"CR11 CR12\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. In recent years, continuous studies have shown that detection methods targeting miRNA and Circulating-tumor DNA (ctDNA) have greatly improved the accuracy of early cancer diagnosis [\\u003cspan additionalcitationids=\\\"CR15\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Y. Sun et al. designed a novel fluorescence sensor based on E36 encapsulated vesicles to detect miRNA-21 through the specific interaction between E36 and miRNA-21 [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. By modifying magnetic microspheres with gold nanoparticles, C. Li et al. constructed a novel nanoMBs-based biosensor for ctDNA detection, which not only showed good stability, but also realized the detect limitation of 0.1 nM with the detection range of 0.2\\u0026ndash;20 nM [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Although the sensitivity related to the early diagnosis of ovarian cancer has been greatly improved by using RNA and ctDNA as targets, its specificity and sensitivity need to be further improved. Most of the existing studies only focus on the detection of ovarian cancer with no more than three targets [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], lacking of efficient multi-channel and multi-component detection methods to further improve the sensitivity and selectivity of the targets related to the early diagnosis of ovarian cancer.\\u003c/p\\u003e \\u003cp\\u003eWith the development of DNA nanostructures, DNA is no longer simply considered as the carrier of genetic information. Due to its unique base-pairing properties, DNA has become the structural unit of constructing nanomaterials [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e], playing a significant role in the high-sensitivity detection of nucleic acids, proteins, viruses, bacteria, and other targets. Especially in the detection of nucleic acid-based targets, which shows a promising prospect for the early diagnosis and treatment of cancer. J. Li et al. developed a Cayley tree-like fractal DNA framework with topological encoding of fluorescence states for multiplex detection of low-abundance targets [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Such structure allows modular design of DNA nanostructures with tunable mechanical properties, providing a highly versatile toolkit for multiplexing and quantitative detection of low-abundance biological targets. W. Diao et al. developed a surface plasmon resonance (SPR) biosensing strategy based on entropy-driven strand displacement reaction (ESDR) and double-layer DNA tetrahedra (DDT) for highly sensitive detection of HIV-related DNA [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. In addition, the application of DNA nanostructure for the construction of fluorescent switching system has also shown good sensitivity and specificity for the early diagnosis of cancer. X. Y. Yang et al. built a spherical recognition probe and toe-mediated strand displacement reaction-induced silver nanocluster (AgNCs) fluorescence signal switch system for the ct DNA detection related to Alzheimer's disease.\\u003csup\\u003e22\\u003c/sup\\u003e Due to the advantages of programmability, biocompatibility and biodegradability, DNA nanostructures have shown potential prospects for application in biological detection, particularly in the construction of multi-channel nano-reaction platform and the detection of multi-component nucleic acid targets.\\u003c/p\\u003e \\u003cp\\u003eAt present, biosensors with fluorescence, electrochemical, Raman and colorimetry as signal outputs have been constructed for the detection of early cancer biomarkers. Among them, fluorescence signal has been widely used because of its simplicity, high specificity and high sensitivity in ultramicroscopic samples.\\u003csup\\u003e23,24\\u003c/sup\\u003e However, detection methods based on the fluorescence signal as an output are still limited by the ability to detect multiple types of targets in parallel, due to the limitations of the types of fluorescence dye that can be modified into nucleic acids for commercialization.\\u003c/p\\u003e \\u003cp\\u003eTherefore, in order to overcome the above problems and achieve the multi-channel detection of up to ten ctDNA targets related to the early diagnosis of ovarian cancer in this work, firstly, a DNA-nanoswitch-based multi-channel (DNA-NSMC) platform was constructed. By modularizing the DNA-NSMC platform, high sensitivity detection of up to ten targets is realized by using only two fluorescence signals as outputs. In addition, this method can simultaneously segment the fluorescence output signal in multiple channels, effectively avoiding the difficulty of increasing the types of detection targets due to the limitation of the types of commercial fluorophores. Not only that, it can also effectively avoid the situation of signal crosstalk caused by excessive fluorophores. Secondly, in order to further improve the sensitivity, the enzyme-free catalytic hairpin assembly-mediated signal amplification (CHA) was introduced, which was also modularized. It is worth noting that the modular segmentation strategy proposed in this work is scalable. With the increase of detection target types, it is expected that the number of detection targets can be extended up to 2\\u003csup\\u003en\\u003c/sup\\u003e by increasing the number of platform subunits to 2\\u003csup\\u003en\\u003c/sup\\u003e. Based on the above strategies, in this work, we successfully realized the fluorescence detection of ten ctDNA targets associated with early diagnosis of ovarian cancer for the first time, and showed good sensitivity and specificity. In addition, it also showed stable detection characteristics in actual human serum, which provided a new idea for the early diagnosis and treatment of ovarian cancer.\\u003c/p\\u003e\"},{\"header\":\"Experimental section\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eChemicals and Instruments\\u003c/h2\\u003e \\u003cp\\u003eThe DNAs utilized in this study were synthesized by Hippo Biotechnology Company (Zhejiang, China) and the sequences are listed in Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e-S3 (Supporting Information). All DNA samples were dissolved in ultrapure water and quantified using the Thermo Nanodrop One. Chemicals including acrylamide, methylene bisacrylamide, ammonium persulfate, TEMED, Boric acid and EDTA were procured from Aladdin Biochemical Technology Co. LTD. All reagents were of analytical grade without further purification. All the solutions used in this work were prepared by ultrapure water (\\u0026gt;\\u0026thinsp;18 MΩ) obtained from two-stage reverse osmosis purification system. Prior to application, the DNAs were thinned with TE buffer (10\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e M Tris-base, 1\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e M EDTA, pH\\u0026thinsp;=\\u0026thinsp;8.0) before use. The native polyacrylamide gel electrophoresis experiments were conducted using an electrophoresis tank from BeiJing Jun Yi. Gel images were captured using Bio-Rad. The fluorescence emission spectra were collected utilizing the FL6500 fluorescence spectrometer .\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMeasurement of Fluorescence\\u003c/h2\\u003e \\u003cp\\u003eThe fluorescence emission spectra of the output were recorded in TE buffer at room temperature. The excitation wavelength of FAM and HEX are 495 and 535 nm, and emission spectra were measured from 510 to 550 nm and from 540 to 575 nm. The slit widths are set to 5 nm for both excitation and emission.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNative Polyacrylamide Gel Electrophoresis (PAGE)\\u003c/h2\\u003e \\u003cp\\u003ePrior to usage, the DNA solutions were heated at 90\\u0026deg;C for 10 min and gradually cooled to room temperature. Subsequently, a mixture of platforms and targets were added to reach a final volume of 100 \\u0026micro;L and then incubated for 60 min. The 12% PAGE was prepared, and electrophoresis was carried out in 1\\u0026times;TBE buffer (89\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e M Tris-boric, 2\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e M EDTA, pH\\u0026thinsp;=\\u0026thinsp;8.0) at a constant voltage of 100 V for about 60 min. Gels imaging using a gel image system (Bio-Rad Laboratories).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOperation of detecting target\\u003c/h2\\u003e \\u003cp\\u003eThe multi-channel platform based on DNA-nanoswitch (P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e-P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e) that Five centrifuge tubes with equal distribution were utilized as the universal system for the detection of 10 biomarkers of ovarian cancer. Different DNA strands (excluding the target sequences and hairpin structures) solutions were heated to approximately 90\\u0026deg;C for 10 minutes and then allowed to cool gradually to room temperature. In accordance with the reaction requirements, the sequences of p-DNA, f-DNA, q-DNA used for constructing platforms were pre-mixed and subsequently added in equal proportions to five centrifuge tubes containing the reaction platform. Following approximately an hour of reaction time, the target sequences were introduced into all test solutions. After the completion of the reactions, the contents from the centrifuge tubes were carefully transferred to a cuvette. Following this transfer, the output signals were swiftly and efficiently tested using a fluorescence spectrophotometer. The optimized concentrations of target and DNA-nanoswitch-based platform were combined to achieve a final volume of 100 \\u0026micro;L.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSignal amplification design\\u003c/h2\\u003e \\u003cp\\u003eDifferent DNA strands (excluding the target sequences and hairpin structures) solutions were heated to approximately 90\\u0026deg;C for 10 min and gradually cooled to room temperature. In accordance with the reaction requirements, the DNA sequences utilized in constructing platforms were pre-mixed and then added in equal amounts to five centrifuge tubes. Next, the hairpin-structure DNAs involved in the amplification reaction were added to the multi-channel mixtures. When the targets were added and the reactions were complete, the reactants in the centrifuge tube were carefully transferred to the cuvette and the output signals were quickly tested using a fluorescence spectrometer. The optimized concentrations of the target and DNA nanoswitch-based platform were combined to reach a final volume of 100 \\u0026micro;L.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThe design of biosensor\\u003c/h2\\u003e \\u003cp\\u003eIn this work, in order to achieve multi-channel and enzyme-free detection of up to ten targets related to the early diagnosis of ovarian cancer, a DNA-based fluorescence switch set was first constructed and used as the reaction platform for detection, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. In order to overcome the problem that the detection scale is limited due to the difficulty in obtaining ten different kinds of fluorescent dyes with varying emission peaks as output signal markers, the following strategies were adopted in this work to construct an ideal reaction platform for target detection. First, a series of ten monomers based on DNA-nanostructure (P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e, \\u0026hellip; P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e) as the reaction platform was constructed, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea. Each monomer consisted of three DNA single strands, with two strands modified by fluorophore and quenching group, which hybridized with the third single strand. All of the monomers contain only two different fluorescent dyes as modifications. Second, the ten reactive monomers were modularly segmented, and each two switch structures with different fluorescent modifications (FAM and HEX) were used as a subset. Therefore, such 10 reaction monomers were evenly divided into five modules, which were placed in five centrifuge tubes, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb. Accordingly, the ten output signals were partitioned into five separate subsets too. Third, to enhance the sensitivity of the detection system, the amplification reaction based on CHA was introduced in this work, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec. Among them, ten corresponding amplification systems were designed for the detection of ten targets, and modular segmentation was also carried out. By integrating with the loading reaction platform of a cuvette with signal acquisition using fluorescence spectrophotometer, the multi-channel and distinguishable detection of the ten output signals labeled with only two fluorescent dyes is realized. The reaction system showed excellent scalability, allowing for direct expansion of the multi-channel reaction platform by increasing the number of reaction monomers, and then the scale of biological detection can be increased. This strategy shows potential research and application value, which motivates us to continue to explore in the follow-up work to achieve breakthroughs in large-scale detection.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eConstruction validation of the DNA-NSMC platform\\u003c/h2\\u003e \\u003cp\\u003eThe reaction mechanism of ten monomers (P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e, \\u0026hellip; P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e) in the DNA-NSMC-based platform is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea. The optimized concentrations of three single-stranded DNAs comprising each DNA-nanostructure-based monomer has been discussed in Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e and Fig. S2. The toehold-mediated DNA strand displacement (TDST) reaction can occur by introducing one or more targets, facilitating the restoration of fluorescence signals that have been quenched on the reaction platform. Therefore, by monitoring the \\u0026ldquo;on\\u0026rdquo; and \\u0026ldquo;off\\u0026rdquo; of the fluorescence signal, as well as regulating its intermediate state process, the target can be successfully sensed. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb, the background signal intensity of the DNA-NSMC platform before adding the target (column 1) and the fluorescence recovery intensity after adding the target (column 2) are shown, indicating the successful construction of ten reaction monomers based on the DNA-nanoswitch structure. Furthermore, we further proved that even when ten monomers were divided into five subsets, each subset maintained an optimal fluorescence switching effect and exhibited minimal background signal (Fig. S3).\\u003c/p\\u003e \\u003cp\\u003eFurthermore, the structural integrity of the DNA-NSMC platform composed of ten reaction monomers was further demonstrated by native polyacrylamide gel electrophoresis (PAGE) experiments, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec. All the DNA sequences involved in the construction of DNA-NSMC platform have been determined by different belts. Using the formation of the P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e monomer as an example, from Lane-1 to Lane-3, the bands showed the single-stranded p\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, f\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, and q\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e. The locations of q\\u003csub\\u003en\\u0026minus;1\\u003c/sub\\u003e were not obvious, which may be because their molecular weights are too small to stay in the gel. When f\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e was added to the p\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, a new band appeared in Lane-4, indicating the hybridization happened and formed the duplex of p\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e/f\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e. In a similar way, When the q\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e was added to the p\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, a new band appeared in Lane-5, indicating the hybridization happened and formed the duplex of p\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e/q\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e. However, when the three components were mixed together, a new band emerged in Lane-6, indicating the hybridization happened and formed the duplex of p\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e/(f\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e+q\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e), which is the reaction monomer of P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eValidation of CHA reaction\\u003c/h2\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, which has been successfully proved that the introduction of CHA into the reaction system based on DNA-NSMC platform can effectively improve the sensitivity of detection. When there was no target in each subset, the mixture of p-DNA/f-DNA/q-DNA (column 1, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) exhibited low fluorescence intensity, which was attributed to effective control of background in platform and Fluorescence resonance energy transfer (FRET) reaction. When the target was added to the reaction platform without CHA, it can undergo TDST reaction with the platform, leading to the restoration of the fluorescence signal (column 2, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). However, when the addition of target into the reaction platform with CHA, the hybridization product between the target and the platform will further activate the hairpin sequence of H\\u003csub\\u003en\\u003c/sub\\u003e associated with the CHA, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec, thus triggering the amplification reaction and leading to further enhancement of the fluorescence signal (column 3, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). All H-DNA sequences involved in the CHA are listed in Table S2. Comparing with the target detection without CHA reaction, an about 500% fluorescence signal enhancement is observed, which showed up to twice the signal enhancement.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTarget detection and selectivity based on DNA-NSMC platform\\u003c/h2\\u003e \\u003cp\\u003eBy analyzing the dependence of fluorescence signal response with different concentrations of target ctDNA, the sensitivity of this work was evaluated. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea-d, we take the detection of T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e and T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e without (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea and c) and with (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb and d) CHA participation as an example, the corresponding fluorescence intensity gradually increased when the concentration of target ctDNA was raised from 100 to 500 nM. On DNA-nanoswitch-based platform without CHA (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee, f, black correction curve), the fluorescence intensity at 517 nm and 556 nm varies linearly with the increase of T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e and T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e concentrations, and the detection limits (LOD) were 45.51 nM and 29.74 nM (3σ/S, where σ represents the standard deviation of the background signal, and S represents the slope of linear curve), respectively. However, on DNA-nanoswitch-based platform containing CHA (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee, f, red correction curve), the fluorescence intensity at 517 nm and 556 nm showed a steeper linear change with the increase of T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e and T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e concentrations, and the LOD were 1.45 nM and 1.13 nM, respectively, resulting in a 31.5-fold and 26.3-fold lower than that of the detection system without CHA (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee, f). Other DNA-nanoswitch-based reaction subset and their detection characteristics were explained in detail in Fig. S4-S7. Therefore, the proposed DNA-NSMC biosensor based on the multi-module CHA can not only realize multi-component detection under multi-channel, but also show better sensitivity.\\u003c/p\\u003e \\u003cp\\u003eTo assess the specificity of the DNA-based multi-channel biosensor. Four mismatched DNA sequences were chosen as control groups, and the corresponding sequences were listed in Table S3. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eg, h, compared with single-mismatched, three-mismatched, five-mismatched target genes and miRNA-21, the detection system constructed in this work showed a lower response, indicating good detection specificity.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eApplication to detect target in human serum\\u003c/h2\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, the practical application of the DNA-based multi-channel biosensor was conducted using human serum spiked with target gene at various concentrations, taking the detection of T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e and T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e as an example. In this work, different concentrations of target genes were added to 5% and 10% of human serum, respectively, and the analysis results were shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea, b. As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, the spiked recovery in T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e and T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e detection ranged from 86\\u0026ndash;105%, with a relative standard deviation (RSD) below 9.78% in all groups. These results indicate that our prepared DNA biosensor exhibits robust anti-interference capability in complex matrix.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eReal sample assays using human serum for T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e and T\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e gene detection (n\\u0026thinsp;=\\u0026thinsp;3)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSamples\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e gene (nM)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eAssay results (nM)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eRecovery (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eRSD (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5% serum\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10% serum\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5% serum\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10% serum\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5% serum\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e10% serum\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e98.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91.846\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e98.38%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e91.85%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5.89%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.41%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e199.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e202.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99.79%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e101.19%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.59%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.21%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e300\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e299.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e301.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99.91%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100.55%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.70%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.51%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e400\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e405.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e402.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e101.48%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100.55%\\u003c/p\\u003e \\u003c/td\\u003e 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\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e10% serum\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e5% serum\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e10% serum\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e5% serum\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e10% serum\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e101.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e101.29%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100.164%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e9.53%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.56%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e194.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e195.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e97.02%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e97.81%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4.56%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e9.59%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e300\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e299.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e300.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99.83%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100.04%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.78%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.88%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e400\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e402.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e399.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e100.65%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.88%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.99%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.37%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e503.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e496.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e100.63%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.36%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.37%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.45%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn summary, in order to improve the accuracy of early diagnosis of ovary cancer and overcome the difficulty of improving detection sensitivity and specificity due to the limited types of detection targets, in this study, established a DNA-nanoswitch-based multi-channel biosensor utilizing a multi-module catalytic hairpin assembly-mediated signal amplification system. With this strategy, we have successfully achieved fluorescence detection of up to ten ctDNA targets associated with the early diagnosis of ovarian cancer. For large-scale detection targets, including the detection strategy in this study, which involves responding to ten different targets, typically necessitates the use of 10 fluorescent dyes with distinct emission peaks as output signal markers for detection and recognition.However, due to current limitations in the development of science and technology, it is challenging to identify 10 suitable fluorescent dyes. This limitation also represents one of the difficulties encountered in expanding the scale of biological detection. To address this issue, we have adopted the following strategies: (1) a DNA-based fluorescence switch set, including ten DNA\\u0026ndash;nanoswitch monomers (P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, P\\u003csub\\u003e1\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e, \\u0026hellip; P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1\\u003c/sub\\u003e, P\\u003csub\\u003e5\\u0026thinsp;\\u0026minus;\\u0026thinsp;2\\u003c/sub\\u003e), was first constructed and used as the reaction platform for detection; (2) The ten reactive monomers were modularized and evenly divided into five subsets, which were positioned in turn in five holes of the 96-well plate; (3) In order to further improve the sensitivity of the detection system, 10 CHA-based amplification reactions corresponding to each channel are introduced in this work, which are also modularized. In addition, the reaction system demonstrates promising scalability, and the scale of multi-channel reaction platform can be directly expanded by increasing the number of reaction monomers, thus improving the scale of biological detection. The experimental findings suggest that the proposed biosensor holds promise as a tool for the simultaneous detection of multiple biomarkers in ovarian cancer.\\u003c/p\\u003e \\u003cp\\u003eWe commit that in the future, leveraging the design concept of this study, which harnesses the potent DNA chain displacement reaction and CHA, we will continue to explore further to accomplish even larger and more intricate biological assays.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u003c/strong\\u003e\\u0026nbsp; YiWei Song: Writing - original draft, Validation, Methodology, Investigation. Xiuyan Jin:Investigation, Data curation. Shengjun Bu:Supervision, Project. Liming Liu: administration, Conceptualization. Chunyang Zhou: Project administration, Investigation, Writing - original draft and lead. Chunying Pang: Investigation, Supporting.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding \\u0026nbsp;\\u003c/strong\\u003eThis work were supported by Jilin Province Science and Technology Department projects (YDZJ202301ZYTS33, 20220204127YY and 20240101203JC) and National Natural Science Foundation of China (52071048).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability \\u0026nbsp;\\u003c/strong\\u003eData will be made available on request.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of interest \\u0026nbsp;\\u003c/strong\\u003eThere are no conflicts to declare.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSiegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. 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Anal Chim Acta 1110:19-25. https://doi.org/10.1016/j.aca.2020.02.063\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"microchimica-acta\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"miac\",\"sideBox\":\"Learn more about [Microchimica Acta](https://link.springer.com/journal/604)\",\"snPcode\":\"604\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/604/3\",\"title\":\"Microchimica Acta\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Ovarian cancer, DNA nanoswitch, Multi-channel sensing, CHA, ctDNA detection\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4530662/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4530662/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eSingle-level biomarker detection has the limitation of insufficient accuracy in cancer diagnosis. Therefore, the strategy of developing highly sensitive, multi-channel biosensors for multi-component biomarkers analysis is critical to improve the accuracy of early diagnosis of clinical tumors. Herein, in order to achieve efficient detection of up to ten targets for early diagnosis of ovarian cancer, a DNA-nanoswitch-based multi-channel (DNA-NSMC) biosensor was built based on the multi-module catalytic hairpin assembly-mediated signal amplification (CHA) and toehold-mediated DNA strand displacement (TDSD) reaction. In this work, only two different fluorescence signals are used as outputs, combined with modular segmentation strategy of DNA-nanoswitch-based reaction platform, the multi-channel detection of up to 10 targets is successfully achieved for the first time. The experimental results suggest that the proposed biosensor is a promising tool for simultaneously detecting multiple biomarkers for the early diagnosis of ovarian cancer, offering new strategies for the early screening, diagnosis, and treatment not only for ovarian cancer but also for other cancers.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Construction of scalable multi-channel DNA nanoplatform for the combined detection of multi-component biomarkers of cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-06-25 11:45:49\",\"doi\":\"10.21203/rs.3.rs-4530662/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-07-18T08:13:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-07-15T10:15:26+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-07-12T16:38:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"235237768482493294390853564381318824123\",\"date\":\"2024-07-03T14:30:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"307212070821268790094001351924037993884\",\"date\":\"2024-06-20T05:39:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-06-18T04:26:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-06-10T08:52:30+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-06-10T08:51:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Microchimica Acta\",\"date\":\"2024-06-05T01:30:09+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"microchimica-acta\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"miac\",\"sideBox\":\"Learn more about [Microchimica Acta](https://link.springer.com/journal/604)\",\"snPcode\":\"604\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/604/3\",\"title\":\"Microchimica Acta\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"5a6498e9-f581-4d51-a369-1ec47c9885cb\",\"owner\":[],\"postedDate\":\"June 25th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-08-26T16:09:15+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4530662\",\"link\":\"https://doi.org/10.1007/s00604-024-06632-6\",\"journal\":{\"identity\":\"microchimica-acta\",\"isVorOnly\":false,\"title\":\"Microchimica Acta\"},\"publishedOn\":\"2024-08-21 15:57:41\",\"publishedOnDateReadable\":\"August 21st, 2024\"},\"versionCreatedAt\":\"2024-06-25 11:45:49\",\"video\":\"\",\"vorDoi\":\"10.1007/s00604-024-06632-6\",\"vorDoiUrl\":\"https://doi.org/10.1007/s00604-024-06632-6\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4530662\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4530662\",\"identity\":\"rs-4530662\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}