温度不敏感的 Aβ 蛋白浓度荧光检测技术研究

Objective Alzheimer’s disease (AD) represents the predominant form of dementia, constituting 60% ‒80% of all dementia cases. The aggregation and oligomerization of β-amyloid (Aβ) in the brain represent hallmark pathological features of AD. While cerebrospinal fluid (CSF) analysis remains the current gold standard requiring a lumbar puncture, plasma-based Aβ detection presents a less invasive and more accessible diagnostic approach. Early plasma-based Aβ detection enables timely intervention and potentially decelerates AD progression, rendering this research significant for clinical and scientific communities. Plasma Aβ level detection is essential for early diagnosis; however, the extremely low mass concentrations of Aβ40 (270‒290 pg/mL) and Aβ42 (30‒40 pg/mL) present significant detection challenges. Ultrasonic cavitation-based β-amyloid fibril proliferation technology enables protein fibril amplification using trace seeds, and when integrated with fluorescent probe detection technology, facilitates trace β-amyloid detection and fibril aggregation process observation. However, high-power ultrasonic processing and varying incubation periods induce sample temperature fluctuations between 25 ℃ and 40 ℃ . Since fluorescent probe excitation and emission spectra demonstrate temperature sensitivity, these variations can affect fluorescence intensity measurements, potentially compromising the accuracy of β-amyloid concentration determination and diagnostic result reliability. Methods We selected Thioflavin T (ThT) as the fluorescent detection probe, an established fluorescent probe that selectively binds to Aβ aggregates to form ThT-amyloid fibril complexes. When excited by a 440 nm light source, this complex emits fluorescence at 480 nm, with the fluorescence signal intensity linearly correlating to the Aβ aggregate content in the sample. To address temperature fluctuation effects from high-power ultrasonic processing on fluorescence intensity detection, we examined the temperature sensitivity of excitation efficiency for light sources with varying spectral linewidths, ultimately selecting an optimally linewidth-configured excitation source. Given that ThT’s fluorescence emission spectrum exhibits a red shift with increasing sample temperature, and to eliminate non-fluorescent stray light interference, we investigated fluorescence spectral filtering techniques and examined temperature effects on detected fluorescence intensity using filters of varying bandwidths. To satisfy high fluorescence detection sensitivity and linearity requirements, we implemented single-photon detection technology. Considering the low Aβ sample concentrations and resultant weak fluorescence emissions, we analyzed system stray light sources and developed a combined spectral filtering-based stray light suppression technique to attenuate stray light below the single-photon detector’s equivalent noise power. Results and Discussions Regarding the impact of the excitation light source on excitation efficiency, we found that when the spectral linewidth of the excitation light source was 2 nm, the relative change in the excitation spectrum overlap integral decreased by approximately 67% compared to a light source with a commonly used 10 nm linewidth [Fig. 3(b)], indicating that the excitation light absorption efficiency is less sensitive to sample temperature changes. Regarding the effect of using filters with different bandwidths on the temperature sensitivity of detected fluorescence intensity, the study found that when using a fluorescence filter with a bandwidth of 40 nm (passband of 460 nm to 500 nm), the detected fluorescence intensity was least sensitive to sample temperature changes [Fig. 5(b)]. The combined spectral filtering-based stray light suppression technique ultimately controlled the background noise of the detection device to 7 photons (Fig. 17). Experimental measurements on plasma samples with different Aβ concentrations showed that the temperature sensitivity of the detected fluorescence intensity was better than 2.1% [Fig. 18(b)]. Further experiments, by adjusting the output power of the light source to simulate the fluorescence intensity of plasma samples with different Aβ concentrations, demonstrated that within a dynamic range of 957358, the linear fitting coefficient of determination (R²) between the count value and the detected fluorescence intensity reached 0.9996 [Fig. 20(b)]. Conclusions The developed fluorescence detection device demonstrated robust performance in experimental measurements of plasma samples with varying Aβ concentrations. The measured fluorescence intensity count values exhibited strong linear correlation with Aβ concentration, achieving a linear fitting coefficient of determination of 0.9996. The relative standard deviation across 50 measurements of identical concentration samples remained below 1.3%, indicating excellent measurement stability. Additionally, utilizing a light source with adjustable output power to simulate plasma sample fluorescence intensity at different Aβ concentrations revealed the relationship between count values and fluorescence emission powers. The experimental results demonstrated a linear counting dynamic range of 957358, maintaining a linear fitting coefficient of determination of 0.9996 throughout the entire range. These findings confirm that the developed Aβ concentration fluorescence detection technology achieves high-sensitivity weak fluorescence signal detection, satisfying the requirements for low concentration Aβ detection in plasma samples and advancing early Alzheimer’s disease diagnosis capabilities.