雜交鍊式反應應用於相位表面電漿子共振感測器

The Utilization of Hybridization Chain Reaction on Phase-Sensitive Surface Plasmon Resonance

楊景旭(Ching-Hsu Yang)

指導教授 ： 黃念祖

共同指導教授 ： 林啟萬(Chii-Wann Lin)

國立臺灣大學/電機資訊學院/生醫電子與資訊學研究所/博士(2022年)

https://doi.org/10.6342/NTU202200658

摘要

在臨床生化檢測與分析化學領域中，表面電漿子共振生物感測器因具有即時性、免標識、非接觸式等優點，提供分子交互作用的定性與定量動態訊息，有許多成功的研究成果。然而，應用表面電漿子共振生物感測器來檢測樣品，特別是在復雜的生物樣品中，受到非特異性結合和信號差的限制。近年來，科學家研發多種用於支持更好檢測的擴增方法來克服這一缺陷。在生物樣品中，核酸，尤其是 DNA 適體，成為一種目標檢測工具。
雜交鏈式反應（Hybridization Chain Reaction，HCR）是一種只需三種DNA：線狀引子DNA、兩個環狀H1與H2的DNA鏈取代反應，是不需要酵素的等溫信號放大技術。在雜交鏈式反應中，引子引發兩種環狀DNA交替開環，自組裝，得到包含大量重覆單元的線性雙鏈DNA奈米結構，具有恆溫、免酵素、放大效率高等優點。結合相位式表面電漿子共振感測器和雜交鏈式反應的實驗中，在最佳鹽條件下，隨著 H1 和 H2 濃度的增加，HCR 信號增強，導致信號放大在 30 分鐘時達到檢測量的 6.5 倍。
我們使用雜交鏈式反應檢測嚴重急性呼吸道症候群冠狀病毒2型（Severe acute respiratory syndrome coronavirus 2，SARS-CoV-2）N1/N2/N3 基因座和人類核糖核酸酶 P的互補 DNA (cDNA)，並開發雜交鏈式反應的演算法。該演算法有助於搜索具有低局部二級結構和高雜交性能的目標序列。雜交鏈式反應中 H1 和 H2的環域是此類反應中的可調片段，用作優化參數以提高鍊式反應的雜交效率。演算法衍生的雜交鏈式反應反應透過凝膠電泳驗證，反應都表現出分子質量 > 1.5k 鹼基對的雜交複合物。凝膠電泳的雜交效率趨勢與演算法的模擬結果很好地對應。
癌症轉移是一種發病率高和診斷難度較大的疾病。近年研究顯示，胞泌體在癌症轉移的過程扮演重要作用。我們開發靈敏的相位式表面電漿共振感測器系統，設計雙功能適體，結合 HCR 反應對胞泌體進行檢測，希望未來有機會透過這個檢測方式，更提早發現病人癌症轉移的問題。
相位式表面電漿共振子感測器以其高靈敏度聞名，較強度式表面電漿共振子感測器靈敏，對折射率之解析度至少高一個數量級。此技術有一個瓶頸，在固定角度設置下測量時，結果重現性低。這是一個尚未被充分討論的關鍵問題。一種可能的解決方案是通過簡單的非線性擬合，映射到折射率單位。然而，基本擬合函數不能很好地描繪非對稱相位曲線。另一方面，基於菲涅耳係數的多層反射率計算，可用於精確映射函數。然而，這種數值方法缺乏用於優化過程的明確數學公式。為此，我們為該問題提供一種方法，其中映射函數是從實驗數據的貝葉斯優化多層模型構建的。我們以量測轉移性癌症胞泌體的數據，使用多層模型作為優化試驗函數，透過分段多項式逼近的方法，提出了一種可視化方法來評估優化模型的擬合優度。

關鍵字

雜交鏈式反應 ； 雙功能適體 ； 嚴重急性呼吸道症候群冠狀病毒2型 ； 癌症轉移 ； 胞泌體 ； 相位式表面電漿共振感測器 ； 演算法

並列摘要

Surface Plasmon Resonance (SPR) is popularly used in biological and chemical sensing for the applications are extensive by the fascinating chemical, optical and catalytic properties. However, the application of SPR to detect trace targets, particularly in complex biological samples, is hampered by non-specific binding and poor signal. A variety of approaches for amplification to support better detection have been explored to overcome this deficiency. In biological samples, nucleic acids, especially DNA aptamers, are considered a versatile target detection tool.
Hybridization chain reaction (HCR) is an enzyme-free DNA amplification method of high efficiency operated at room temperature in which two stable species of DNA hairpins coexist in the solution until the introduction of initiator DNA strand triggers a cascade of hybridization events.
HCRs to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS – CoV-2) nucleocapsid phosphoproteins gene loci and human RNase P are proposed to provide an isothermal amplification screening method. The proposed HCRs target the complementary DNA (cDNA) of SARS – CoV-2, with loci corresponding to the gold-standard polymerase chain reaction (PCR) loci. Four hybridization chain reactions are seen here, targeting N1 / N2 / N3 loci and human RNase P. The construction of the hybridization chain reaction is supported by an algorithm. The algorithm helps to search for target sequences with low local secondary structure and high hybridization performance. The loop domain of the H1 and H2 fuel pin molecules, which are tunable segments in such reactions, is used as an optimization parameter to increase the hybridization efficiency of the chain reaction. Algorithm-derived HCR reactions have been validated with gel electrophoresis. Both suggested reactions exhibit a hybridization complex with a molecular mass > 1.5k base pair, which is strong evidence of a chain reaction. The hybridization efficiency trend revealed by the gel electrophoresis corresponds well to the algorithm's simulated results. The HCR reactions and the corresponding algorithm serve as the basis for more SARS – CoV-2 sensing applications and promote improved screening strategies for the prevention of ongoing pandemics.
Cancer metastasis is a difficult disease to cope with. The responsive methodology is important for accurate clinical use and remains unmet. Recent research has shown the significant role of exosomes in the growth of cancer metastases. Exosome-based liquid biopsy has therefore become the prevalent cancer diagnostic study. Our work has further extended to develop a sensitive phase-sensitive Surface Plasmon Resonance (pSPR) system for highly informative sensing of cancerous exosomes using bifunctional aptamers coupled with HCR amplification. The enhancement of more diverse recognition events can be accomplished by integrating HCR with aptamer stimuli tested by SPR. This feature enables DNA to serve as an amplifying transducer for biosensing applications. Our work established a pSPR system for highly informative sensing using HCR for amplification.
High sensitivity is a characteristic of pSPR biosensors. One of these sensors' technical bottlenecks is that the phase sensorgram, when measured at a fixed angle set-up, might result in poor repeatability since the signal carries various data. As a result, maximizing sensitivity while maintaining acceptable reproducibility is an under-discussed critical topic. One such technique is to utilize sensor calibration data to transfer the phase sensorgram into refractive index units through a non-linear fit. The asymmetric phase curve, however, is poorly represented by fundamental fitting algorithms. An exact mapping function, on the other hand, may be achieved by using a multi-layer reflectivity calculation based on the Fresnel coefficient. This numerical method, on the other hand, lacks the clear mathematical formulation required for optimization. To that end, we propose a first technique for the problem, in which mapping functions are built from a Bayesian optimal multi-layer model of experimental data. Meta-modeling using segmented polynomial approximation overcomes the difficulty of employing a multi-layer model as an optimization trial function. The goodness-of-fit on the improved model is evaluated using a visualization technique. We show how the current study paves the way for improved plasmonic sensors by using metastatic cancer exosome sensing.

並列關鍵字

Hybridization Chain Reaction ； Bifunctional Aptamer ； SARS – CoV-2 ； Cancer Metastasis ； Exosomes ； Phase-sensitive Surface Plasmon Resonance ； Algorithm

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