Fully integrated smart microfluidic platform for in situ exosome analysis and classification : 통합형 스마트 미세 유체 플랫폼을 이용한 현장 엑소좀 분석 및 분류

다국어 초록 (Multilingual Abstract)

With the growing need for early-stage disease diagnosis, research has been in- creasingly focused on analyzing the physical properties of biomarkers. Although conventional optical methods offer high sensitivities, they face miniaturization challenges due to their reliance on external light sources. Microfluidic resistive pulse sensing provides a simpler alternative for detecting and analyzing micropar- ticles in various fields such as environmental, chemical, biomedical, and disease diagnostics. This study introduces a microfluidic platform designed to analyze and identify the physical properties of cancer-derived exosomes. Fluid injection within the microfluidic chip is fully automated, utilizing a capillary- and vacuum-chamber- assisted passive-driven technology. To ensure precise measurements, particles are hydrodynamically focused without the need for a sheath, and a reference gate is used to minimize noise during resistive pulse detection. Using this microfluidic ap- proach, the proposed microfluidic chip accurately measures the size, concentration and zeta potential of exosomes derived from MCF-7, MDA-MB-231, and MCF- 10A cell lines with high sensitivity. Additionally, deep learning algorithms are ap- plied to classify and identify the exosomes based on the collected data with accu- racy of 96.6%. The microfluidic platform offers high sensitivity in the analysis and classification of exosome physical properties, making it suitable for potential uses in in vitro clinical applications. Keywords: Microfluidics, Exosome Analysis, Resistive Pulse Sensing, Ma- chine Learning

국문 초록 (Abstract)

질병 조기 진단의 필요성이 증가함에 따라 바이오마커의 물리적 특성을 분석하는 연구가 활발히 이루어지고 있다. 기존의 광학적 방법들은 높은감도를 제공하지만 외부 광원에 의존한다는 점에서 소형화에 어려움을 겪는다.
저항성 펄스 센싱은 환경, 화학, 생물의학, 질병 진단 등 다양한 분야에서 미세입자를 감지하고 분석하는 간단한 대안을 제공한다. 본 연구에서는 암유래 엑소좀의 물리적 특성을 분석하고 식별하기 위해 설계된 미세유체플랫폼을 소개한다. 미세유체 칩 내의 유체 주입은 모세관 현상 및 진공챔버를 이용해 자동화가 가능하다. 정밀한 측정을 위해, 입자의 위치는 유체
역학적으로 제어되며, 저항 펄스 감지 중 잡음을 최소화하기 위해 참조게이트가 사용된다. 이 미세유체 접근 방식을 사용하여 제안된 미세유체 칩은MCF-7, MDA-MB-231, 및 MCF-10A 세포에서 유래된 엑소좀의 크기, 농도 및 제타 전위를 높은 감도로 정확하게 측정한다. 또한, 수집된 데이터를 기반으로 엑소좀을 분류하고 식별하기 위해 딥러닝 알고리즘을 적용하여 96.6%의 정확도를 달성한다. 이 미세유체 플랫폼은 엑소좀의 물리적 특성을 분석하고 분류하는 데 높은 감도를 제공하여 체외 임상 응용에 적합하다.

목차 (Table of Contents)

• 1. INTRODUCTION １
• 2. BACKGROUNDS 12
• 2.1 Optical Methods 12
• 2.1.1. Nanoparticle Tracking Analysis 12
• 2.1.2 Dynamic Light Scattering 19
• 2.2 Resistive Pulse Sensing Method 25
• 2.2.1 Coulter Principle 25
• 2.2.2 Particle Size Calculation 29
• 2.2.3 Zeta Potential Measurement. 33
• 2.3 Particle Dynamics in Microfluidic System 40
• 2.4 DNN network 43
• 3. EXPERIMENTAL 54
• 3.1 Device Design 54
• 3.2 Fabrication Method 62
• 3.3 Sample preparation 65
• 3.4 Experimental Setup 66
• 4. RESULTS AND DISCUSSION 69
• 4.1 Automatic Fluid Injection by Passive Driven Microfluidics. 69
• 4.2 Nanoparicle Analysis by Resistive Pulse Sensing 79
• 4.3 Exosome Analysis Evaluation 92
• 4.4 Calibration of Zeta Potential. 92
• 4.5 Exosome Classification by DNN 115
• 5. CONCLUSION 132
• 초 록 134

참고문헌 (Reference)

1. Small, Feng, Y., Zhu, J., Wang, W., He, W., Huang, L., Chai, H., 19 (45), 2303416, , 2023

2. Comput, Boulesteix, A.-L., Tutz, G., Janitza, S., 96, 57−73, , 2016

3. Biosens, Liang, M., Ai, Y., Tang, Q., Zhong, J., 2023,225, No. 115086, , 2022

4. Lab Chip, He, W., Chai, H., Wang, W., Cheng, Z., Feng, Y., Huang, L., 22 (2), 240−249, , 2022

5. Lab Chip, Spencer, D., Bisegna, P., Morgan, H., Caselli, F., Reale, R., De Ninno, A., 22 (9), 1714−1722, , 2022

6. Biomicrofluidics, Doğan, Z., Rabbi, F., Yetisen, A. K., Tasoglu, S., Dabbagh, S. R., 14 (6), No. 061506, , 2020

7. Sensors Actuators BChem, Hosokawa, Y., Yamamoto, K., Tanaka, Y., Kiya, R., Yamazaki, Y., Li, M., Kamikubo, H., Ota, N., Suetsugu, S., Zhang, T., Yuan, Y., Yang, Y., Tang, T., Liu, X., Yalikun, Y., 2023, 374 (August 2022), No. 132698., , 2023

8. The cancer biomarker problem, Sawyers, C. L., 452, 548-552, , 2008

9. A Method for Stochastic Optimization, Ba, J. Adam, Kingma, D. P., CoRR, , 2014

10. What are the biomarkers for glaucoma?, Flammer, J., Golubnitschaja, O., 52, S155-S161, , 2007

11. Exosome: emerging biomarker in breast cancer, J. Zhou., X. Luo, H. Xiong, B. Xu, W. Hu, L. Wang, Jia, Y., C. Chen, Q. Wei, Q. Wang, J. Wang, Y. Chen, W. Zhao, U. Jayasinghe, 8, 41717, , 2017

12. Fabrication of nanochannels on polystyrene surface, Peng, R., Li, D., 9, , 2015

13. On the interpretation of electrokinetic potentials, Lyklema, J., Overbeek, J. Th. G., 16, 501-512, , 1961

14. Particle characterization: Light scattering methods, R. Xu, Kluwer Academic, , 2001

15. High-Throughput Single Extracellular Vesicle Profiling, Cai, Y., Wu, D., IntechOpen. doi: 10.5772/intechopen.97544, , 2022

16. Choosing the right cell line for breast cancer research, Holliday, D. L., Speirs, V., 13, 1-7, , 2011

17. Edge machine learning for aienabled iot devices: A review, Porcaro, C., Iero, D., Merenda, M., 20(9), 2533, , 2020

18. Role of symptoms in diagnosis and outcome of gastric cancer, Maconi, G., Manes, G., Porro, G. B., WJG 14, 8, 1149, , 2008

19. Zeta potential in colloid science: principles and applications, Hunter, R. J., Vol. 2 Academic Press, , 2013

20. The electrical double layer around a spherical colloid particle, Loeb, A. L., Overbeek, J. T. G., King, C. V., Wiersema, P. H., 108, 269C, , 1961

21. Surface charge modulated aptasensor in a single glass conical nanopore, Zheng, Y. B., Cao, S. H., Zhao, S., Yang, J. L., Li, Y. Q, Cai, S. L., 71, 37-43, , 2015

22. Hydrophilic and size-controlled graphene nanopores for protein detection, Lee, Y. B., Kim, M. J., Ahn, C. W., Darvish, A., Goyal, G., 27, 49, 495301, , 2016

23. Dynamic light scattering: with applications to chemistry, biology, and physics, B. J. Berne, R. Pecora, Dover Publications, Dover edn, , 2000

24. Automated Fabrication of 2‐nm Solid‐State Nanopores for Nucleic Acid Analysis, Kwok, H., Briggs, K., Tabard‐Cossa, V., 10, 2077-2086, , 2014

25. Effect of exosome biomarkers for diagnosis and prognosis of breast cancer patients, Wu, A., Shao, G., Zhang, J., Wang, M., Zhao, K., Wang, Z., Ji, S., 20, 906-911, , 2018

26. Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis, Steinkraus, D., Simard, P. Y., Platt, J. C., Proceedings of the Seventh International Conference on Document Analysis and Recognitions; IEEE Comput. Soc. Vol.1, pp 958−963, , 2003

27. In-Plane, In-Series Nanopores with Circular Cross Sections for Resistive-Pulse Sensing, Starr, C. A., Jacobson, S. C., Greibe, T., Zhang, M., Harms, Z. D., Zlotnick, A., 16, 5, 7352-7360, , 2022

28. Design and demonstration of a novel micro-Coulter counter utilizing liquid metal electrodes, Buckner, G. D., Richards, A. L., Kennedy, A. S., Dickey, M. D., 22, 11, , 2012

29. SERS Detection of Breast Cancer‐Derived Exosomes Using a Nanostructured Pt‐Black Template, Kassanos, P., Yeatman, E., Roddan, A., Keshavarz, M., Li, X., Kidy, Z., Thompson, A. J., 2023, 2, 4, 2200039, , 2023

30. A random forest model for predicting exosomal proteins using evolutionary information and motifs, Devi, N. L., Raghava, G. P., Sharma, N., Kaur, D., Arora, A., Patiyal, S., 2024, 24, 2300231, , 2024

31. Micro coulter counters with platinum black electroplated electrodes for human blood cell sensing, Liu, M., Tai, Y. C., Zheng, S., 10, 221-231, , 2008

32. Oxidation of nanopores in a silicon membrane: self-limiting formation of sub-10 nm circular openings, Roxhed, N., Schmidt, T., Linnros, J., Zhang, M., Sychugov, I., Sangghaleh, F., 25, 35, , 2014

33. High-throughput multi-gate microfluidic resistive pulse sensing for biological nanoparticle detection, Kim, D. Y., Kong, S. H., Jan,g, N., Han, M., Wang, J., Lee. J. Y., Kim, J. S., Kim, H., Kwon, S. Y., Kim, S. D., Lab Chip 2023, 23, 1945-1953., , 2023

34. Fabrication of polydimethylsiloxane (PDMS) nanofluidic chips with controllable channel size and spacing, Peng, R., Li, D., 16, 3767-3776, , 2016

35. Analysis of vibrational dynamics in cell-substrate interactions using nanopipette electrochemical sensors, Wu, X., Li, D. W., Gong, L. J., Qian, R. C., Wang, X. Y., Lv, J., 2024, 259, 116385, , 2024

36. Presenting symptoms of cancer and stage at diagnosis: evidence from a cross-sectional, population-based study, Abel, G. A., McPhail, S., Swann, R., Rubin, G. P., Lyratzopoulos, G., Elliss-Brookes, L., Koo, M. M., 21, 1, 73-79, , 2020

37. Absorption and scattering by a sphere in Absorption and scattering of light by small particles C. Bohren and R., C. F. Bohren, D. Huffman, D. R. Huffman, ed F. Wiley-VCH Verlag GmbH, Weinheim pp. 83–129, , 2007

38. Fluorescence analysis of circulating exosomes for breast cancer diagnosis using a sensor array and deep learning, Chen, Y. Z., Tan, Y., Jin, Y., Shen, W., Nu, N., Huang, Y., Du, N., Tan, C., Yang, A., HE, X., Dou, W., 7, 1524-1532., , 2022

39. Measurement of refractive index by nanoparticle tracking analysis reveals heterogeneity in extracellular vesicles, Gardiner, C., 3, 25361, , 2014

40. Symptoms and other factors associated with time to diagnosis and stage of lung cancer: a prospective cohort study, N. Hall, Walter, F. M. G. Rubin, K. Mills, J. Emery, H. C. Morris, C. Dobson, C. Bankhead, W. Hamilton, R. C. Rintoul, 112, S6-S13, , 2015

41. Improved dynamic light scattering using an adapt. ive and statistically driven time resolved treatment of correlation data, Corbett, J. C., Malm, A. V., 9, 1, 13519, , 2019

42. Artificial intelligent label-free SERS profiling of serum exosomes for breast cancer diagnosis and postoperative assessment, Xie, Y., Su, X., Li, M., Wen, Y., Zheng, C., 22, 19, 7910-7918, , 2022

43. Charged particles modulate local ionic concentrations and cause formation of positive peaks in resistive-pulse-based detection, Yang, C., Siwy, Z. S., Schiel, M., Vlassiouk, I., Menestrina, J., 118, 5, 2391-2398, , 2014

44. A novel microfluidic resistive pulse sensor with multiple voltage input channels and a side sensing gate for particle and cell detection, Zhou, T., Li, D., Song, Y., Yuan, Y., 1052, 113-123. 113-123, , 2019

45. Ratiometric 3D DNA machine combined with machine learning algorithm for ultrasensitive and high-precision screening of early urinary diseases, Wang, J. H., Yang, T., Wu, N., Li, X., Zhang, X. Y., Xia, J., 15, 12, 19522-19534.19522-19534, , 2021

46. The multifaceted exosome: biogenesis, role in normal and aberrant cellular function, and frontiers for pharmacological and biomarker opportunities, Burczynski, M. E., Hilton, H., Pant, S., 83, 1484-1494, , 2012

47. SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae, Ciloglu, F. U., Kahraman, M., Hora, M., Aydin, O., Gundogdu, A., Tokmakci, M., Analytica Chimica Acta 1221, 340094, , 2022

48. Nanoparticle detection by microfluidic resistive pulse sensor with a submicron sensing gate and dual detecting channels-two stage differential amplifier, Zhang, H., Chon, C. H., Li, D., Song, Y., Pan, X., 155, 2, 930-936, , 2011

49. Exosomal circular RNA circ_0074673 regulates the proliferation, migration, and angiogenesis of human umbilical vein endothelial cells via the microRNA-1200/MEOX2 axis, Huang, Y., Liang, B., Chen, X., Bioengineered 12, 1, 6782-6792, , 2021