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Portable electrochemical micro-workstation platform for simultaneous detection of multiple Alzheimer’s disease biomarkers

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Abstract

Alzheimer’s disease, as a most prevalent type of dementia, is quickly becoming one of the most expensive, lethal, and burdening diseases of this century. Though there are still no efficient therapies, early diagnosis and intervention are important directive significance to clinical works. Here, we develop a portable electrochemical micro-workstation platform consisting of an electrochemical micro-workstation and integrated electrochemical microarray for simultaneously detecting multiple AD biomarkers including Aβ40, Aβ42, T-tau, and P-tau181 in serum. The integrated electrochemical microarray is mainly used for droplet sample manipulation and signal generation. The micro-workstation can regulate signals and transfer the signals to a smartphone by Bluetooth embedded inside. This portable electrochemical micro-workstation platform exhibits excellent analysis performance. The LODs for Aβ40, Aβ42, T-tau, and P-tau181 are 0.125 pg/mL, 0.089 pg/mL, 0.142 pg/mL, and 0.176 pg/mL, respectively, which satisfies the needs of detecting AD biomarkers in serum. The combination of portable micro-workstation and integrated electrochemical microarray provides a promising strategy for the early diagnosis of Alzheimer’s disease and personal healthcare.

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References

  1. Li Y, Haber A, Preuss C, John C, Uyar A, Yang HS, Logsdon BA, Philip V, Karuturi RKM, Carter GW, Initia ADN (2021) Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer’s disease progression. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 13(1):1–14. https://doi.org/10.1002/dad2.12140

    Article  Google Scholar 

  2. Scheltens P, Blennow K, Breteler MMB, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM (2016) Alzheimer’s disease. The Lancet 388(10043):505–517. https://doi.org/10.1016/s0140-6736(15)01124-1

    Article  CAS  Google Scholar 

  3. Marcus C, Mena E, Subramaniam RM (2014) Brain PET in the diagnosis of Alzheimer’s disease. Clin Nucl Med 39 (10):e413–422; quiz e423–416. https://doi.org/10.1097/RLU.0000000000000547

  4. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Dore V, Fowler C, Li QX, Martins R, Rowe C, Tomita T, Matsuzaki K, Ishii K, Ishii K, Arahata Y, Iwamoto S, Ito K, Tanaka K, Masters CL, Yanagisawa K (2018) High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature 554(7691):249–254. https://doi.org/10.1038/nature25456

    Article  CAS  PubMed  Google Scholar 

  5. Brazaca LC, Sampaio I, Zucolotto V, Janegitz BC (2020) Applications of biosensors in Alzheimer’s disease diagnosis. Talanta 210:120644. https://doi.org/10.1016/j.talanta.2019.120644

    Article  CAS  PubMed  Google Scholar 

  6. Obata Y, Murakami K, Kawase T, Hirose K, Izuo N, Shimizu T, Irie K (2020) Detection of amyloid β oligomers with RNA aptamers in AppNL-G-F/NL-G-F mice: a model of arctic Alzheimer’s disease. ACS Omega 5(34):21531–21537. https://doi.org/10.1021/acsomega.0c02134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhou X, Wang S, Zhang C, Lin Y, Lv J, Hu S, Zhang S, Li M (2021) Colorimetric determination of amyloid-beta peptide using MOF-derived nanozyme based on porous ZnO-Co3O4 nanocages. Mikrochim Acta 188(2):56. https://doi.org/10.1007/s00604-021-04705-4

    Article  CAS  PubMed  Google Scholar 

  8. Wang Y, Fan D, Zhao G, Feng J, Wei D, Zhang N, Cao W, Du B, Wei Q (2018) Ultrasensitive photoelectrochemical immunosensor for the detection of amyloid beta-protein based on SnO2/SnS2/Ag2S nanocomposites. Biosens Bioelectron 120:1–7. https://doi.org/10.1016/j.bios.2018.08.026

    Article  CAS  PubMed  Google Scholar 

  9. Song Y, Xu T, Zhu Q, Zhang X (2020) Integrated individually electrochemical array for simultaneously detecting multiple Alzheimer’s biomarkers. Biosens Bioelectron 162:112253. https://doi.org/10.1016/j.bios.2020.112253

    Article  CAS  PubMed  Google Scholar 

  10. Liu Y, Xu Q, Zhang Y, Ren B, Huang L, Cai H, Xu T, Liu Q, Zhang X (2021) An electrochemical aptasensor based on AuPt alloy nanoparticles for ultrasensitive detection of amyloid-beta oligomers. Talanta 231:122360. https://doi.org/10.1016/j.talanta.2021.122360

    Article  CAS  PubMed  Google Scholar 

  11. Wang XZ, Du J, Xiao NN, Zhang Y, Fei L, LaCoste JD, Huang Z, Wang Q, Wang XR, Ding B (2020) Driving force to detect Alzheimer’s disease biomarkers: application of a thioflavine T@Er-MOF ratiometric fluorescent sensor for smart detection of presenilin 1, amyloid beta-protein and acetylcholine. Analyst 145(13):4646–4663. https://doi.org/10.1039/d0an00440e

    Article  CAS  PubMed  Google Scholar 

  12. Fang WK, Liu L, Zhang LL, Liu D, Liu Y, Tang HW (2021) Detection of amyloid beta oligomers by a fluorescence ratio strategy based on optically trapped highly doped upconversion nanoparticles-SiO2@metal-organic framework microspheres. Anal Chem 93(36):12447–12455. https://doi.org/10.1021/acs.analchem.1c02679

    Article  CAS  PubMed  Google Scholar 

  13. Yang JK, Hwang IJ, Cha MG, Kim HI, Yim D, Jeong DH, Lee YS, Kim JH (2019) Reaction kinetics-mediated control over silver nanogap shells as surface-enhanced Raman scattering nanoprobes for detection of Alzheimer’s disease biomarkers. Small 15(19):e1900613. https://doi.org/10.1002/smll.201900613

    Article  CAS  PubMed  Google Scholar 

  14. Miranda OR, Chen HT, You CC, Mortenson DE, Yang XC, Bunz UH, Rotello VM (2010) Enzyme-amplified array sensing of proteins in solution and in biofluids. J Am Chem Soc 132(14):5285–5289. https://doi.org/10.1021/ja1006756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chi J, Shao C, Du X, Liu H, Gu Z (2018) Generating microdroplet array on photonic pseudo-paper for absolute quantification of nucleic acids. ACS Appl Mater Interfaces 10(45):39144–39150. https://doi.org/10.1021/acsami.8b11552

    Article  CAS  PubMed  Google Scholar 

  16. Nie B, Li R, Brandt JD, Pan T (2014) Iontronic microdroplet array for flexible ultrasensitive tactile sensing. Lab Chip 14(6):1107–1116. https://doi.org/10.1039/c3lc50994j

    Article  CAS  PubMed  Google Scholar 

  17. Welch CJ (2019) High throughput analysis enables high throughput experimentation in pharmaceutical process research. Reaction Chemistry & Engineering 4(11):1895–1911. https://doi.org/10.1039/c9re00234k

    Article  CAS  Google Scholar 

  18. Xu T, Song Y, Gao W, Wu T, Xu LP, Zhang X, Wang S (2018) Superwettable electrochemical biosensor toward detection of cancer biomarkers. ACS Sens 3(1):72–78. https://doi.org/10.1021/acssensors.7b00868

    Article  CAS  PubMed  Google Scholar 

  19. Song YC, Xu TL, Song X, Zhang XJ (2020) Integrated microdroplets array for intelligent electrochemical fabrication. Advanced Functional Materials 30 (13). https://doi.org/10.1002/adfm.201910329

  20. Mark D, Haeberle S, Roth G, von Stetten F, Zengerle R (2010) Microfluidic lab-on-a-chip platforms: requirements, characteristics and applications. Chem Soc Rev 39(3):1153–1182. https://doi.org/10.1039/b820557b

    Article  CAS  PubMed  Google Scholar 

  21. Kunding AH, Busk LL, Webb H, Klafki HW, Otto M, Kutter JP, Dufva M (2018) Micro-droplet arrays for micro-compartmentalization using an air/water interface. Lab Chip 18(18):2797–2805. https://doi.org/10.1039/c8lc00608c

    Article  CAS  PubMed  Google Scholar 

  22. Mao P, Cao L, Li Z, You M, Gao B, Xie X, Xue Z, Peng P, Yao C, Xu F (2021) A digitalized isothermal nucleic acid testing platform based on a pump-free open droplet array microfluidic chip. Analyst 146(22):6960–6969. https://doi.org/10.1039/d1an01373d

    Article  CAS  PubMed  Google Scholar 

  23. Brutin D, Starov V (2018) Recent advances in droplet wetting and evaporation. Chem Soc Rev 47(2):558–585. https://doi.org/10.1039/c6cs00902f

    Article  CAS  PubMed  Google Scholar 

  24. Dong R, Zhang T, Feng X (2018) Interface-assisted synthesis of 2D materials: trend and challenges. Chem Rev 118(13):6189–6235. https://doi.org/10.1021/acs.chemrev.8b00056

    Article  CAS  PubMed  Google Scholar 

  25. Feng W, Ueda E, Levkin PA (2018) Droplet microarrays: from surface patterning to high-throughput applications. Adv Mater 30(20):e1706111. https://doi.org/10.1002/adma.201706111

    Article  CAS  PubMed  Google Scholar 

  26. He X, Xu T, Gao W, Xu LP, Pan T, Zhang X (2018) Flexible superwettable tapes for on-site detection of heavy metals. Anal Chem 90(24):14105–14110. https://doi.org/10.1021/acs.analchem.8b04536

    Article  CAS  PubMed  Google Scholar 

  27. Zhan SS, Pan Y, Gao ZF, Lou XD, Xia F (2018) Biological and chemical sensing applications based on special wettable surfaces. Trac-Trends in Analytical Chemistry 108:183–194. https://doi.org/10.1016/j.trac.2018.09.001

    Article  CAS  Google Scholar 

  28. Li ZH, Song YC, Fan C, Xu TL, Zhang XJ (2021) Mini-pillar based multi-channel electrochemical platform for studying the multifactor silver electrodeposition. Electroanalysis 33(12):2401–2405. https://doi.org/10.1002/elan.202100462

    Article  CAS  Google Scholar 

  29. Kang J, Lee DW, Hwang HJ, Yeon SE, Lee MY, Kuh HJ (2016) Mini-pillar array for hydrogel-supported 3D culture and high-content histologic analysis of human tumor spheroids. Lab Chip 16(12):2265–2276. https://doi.org/10.1039/c6lc00526h

    Article  CAS  PubMed  Google Scholar 

  30. Li J, Tan W, Xiao W, Carney RP, Men Y, Li Y, Quon G, Ajena Y, Lam KS, Pan T (2018) A plug-and-play, drug-on-pillar platform for combination drug screening implemented by microfluidic adaptive printing. Anal Chem 90(23):13969–13977. https://doi.org/10.1021/acs.analchem.8b03456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee W, Kim D, Rivnay J, Matsuhisa N, Lonjaret T, Yokota T, Yawo H, Sekino M, Malliaras GG, Someya T (2016) Integration of organic electrochemical and field-effect transistors for ultraflexible, high temporal resolution electrophysiology arrays. Adv Mater 28(44):9722–9728. https://doi.org/10.1002/adma.201602237

    Article  CAS  PubMed  Google Scholar 

  32. Song Y, Xu T, Xiu J, Zhang X (2020) Mini-pillar microarray for individually electrochemical sensing in microdroplets. Biosens Bioelectron 149:111845. https://doi.org/10.1016/j.bios.2019.111845

    Article  CAS  PubMed  Google Scholar 

  33. Teng IT, Li X, Yadikar HA, Yang Z, Li L, Lyu Y, Pan X, Wang KK, Tan W (2018) Identification and characterization of DNA aptamers specific for phosphorylation epitopes of tau protein. J Am Chem Soc 140(43):14314–14323. https://doi.org/10.1021/jacs.8b08645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Supraja P, Tripathy S, Vanjari SRK, Singh R, Singh V, Singh SG (2021) Label-free detection of β-amyloid (1-42) in plasma using electrospun SnO2 nanofiber based electro-analytical sensor. Sensors and Actuators B: Chemical 346. https://doi.org/10.1016/j.snb.2021.130522

  35. Zubiate P, Urrutia A, Zamarreño CR, Egea-Urra J, Fernández-Irigoyen J, Giannetti A, Baldini F, Díaz S, Matias IR, Arregui FJ, Santamaría E, Chiavaioli F, Del Villar I (2019) Fiber-based early diagnosis of venous thromboembolic disease by label-free D-dimer detection. Biosensors and Bioelectronics: X 2:100026. https://doi.org/10.1016/j.biosx.2019.100026

    Article  CAS  Google Scholar 

  36. Esposito F, Sansone L, Srivastava A, Baldini F, Campopiano S, Chiavaioli F, Giordano M, Giannetti A, Iadicicco A (2021) Long period grating in double cladding fiber coated with graphene oxide as high-performance optical platform for biosensing. Biosens Bioelectron 172:112747. https://doi.org/10.1016/j.bios.2020.112747

    Article  CAS  PubMed  Google Scholar 

  37. Chan HN, Xu D, Ho SL, Wong MS, Li HW (2017) Ultra-sensitive detection of protein biomarkers for diagnosis of Alzheimer’s disease. Chem Sci 8(5):4012–4018. https://doi.org/10.1039/c6sc05615f

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kim K, Lee CH, Park CB (2020) Chemical sensing platforms for detecting trace-level Alzheimer’s core biomarkers. Chem Soc Rev 49(15):5446–5472. https://doi.org/10.1039/d0cs00107d

    Article  CAS  PubMed  Google Scholar 

  39. Zhang X, Liu S, Song X, Wang H, Wang J, Wang Y, Huang J, Yu J (2019) Robust and universal SERS sensing platform for multiplexed detection of Alzheimer’s disease core biomarkers using PAapt-AuNPs conjugates. ACS Sens 4(8):2140–2149. https://doi.org/10.1021/acssensors.9b00974

    Article  CAS  PubMed  Google Scholar 

  40. Kim H, Lee JU, Song S, Kim S, Sim SJ (2018) A shape-code nanoplasmonic biosensor for multiplex detection of Alzheimer’s disease biomarkers. Biosens Bioelectron 101:96–102. https://doi.org/10.1016/j.bios.2017.10.018

    Article  CAS  PubMed  Google Scholar 

  41. Kim HJ, Ahn H, Kim H, Park D, Lee JS, Lee BC, Kim J, Yoon DS, Hwang KS (2022) Nanoparticle-based multiplex biosensor utilising dual dielectrophoretic forces for clinical diagnosis of Alzheimer’s disease. Sens Actuators, B Chem 355:131288. https://doi.org/10.1016/j.snb.2021.131288

    Article  CAS  Google Scholar 

  42. Kwon SS, Kim D, Yun M, Son JG, Lee SH (2021) The role of graphene patterning in field-effect transistor sensors to detect the tau protein for Alzheimer’s disease: simplifying the immobilization process and improving the performance of graphene-based immunosensors. Biosens Bioelectron 192:113519. https://doi.org/10.1016/j.bios.2021.113519

    Article  CAS  PubMed  Google Scholar 

  43. Wang YR, Chuang HC, Tripathi A, Wang YL, Ko ML, Chuang CC, Chen JC (2021) High-sensitivity and trace-amount specimen electrochemical sensors for exploring the levels of beta-amyloid in human blood and tears. Anal Chem 93(22):8099–8106. https://doi.org/10.1021/acs.analchem.0c04980

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the Instrumental Analysis Center of Shenzhen University (Xili Campus) for providing access to the instruments used in the experiments.

Funding

The work was supported by Longgang District Medical and health science and technology project (LGKCYLWS2021000003), the National Natural Science Foundation of China (21804007, 21890742), SZU Top Ranking Project (86000000210), Shenzhen Key Laboratory for Nano-Biosensing Technology (ZDSYS20210112161400001), and Shenzhen Stability Support Plan (20200806163622001).

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Correspondence to Qiong Liu or Tailin Xu.

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Yibiao Liu, Zhen Huang, and Qing Xu are co-first authors

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Liu, Y., Huang, Z., Xu, Q. et al. Portable electrochemical micro-workstation platform for simultaneous detection of multiple Alzheimer’s disease biomarkers. Microchim Acta 189, 91 (2022). https://doi.org/10.1007/s00604-022-05199-4

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