本文選題：多角度TIRF成像 + Glut4囊泡��； 參考：《浙江大學(xué)》2016年博士論文

【摘要】：隨著光學(xué)顯微技術(shù)的進(jìn)步,全內(nèi)反射熒光顯微鏡(total internal reflection fluorescence microscopy, TIRFM)相比于傳統(tǒng)的生物醫(yī)學(xué)研究方法(如免疫細(xì)胞化學(xué)、基因技術(shù)、亞細(xì)胞分層等)能夠?qū)崟r(shí)觀察細(xì)胞膜附近區(qū)域內(nèi)的亞細(xì)胞目標(biāo)(如轉(zhuǎn)運(yùn)囊泡、自噬溶酶體、細(xì)胞骨架等),并定量分析其結(jié)構(gòu)形態(tài)和動(dòng)態(tài)運(yùn)動(dòng)特性(如微管結(jié)構(gòu),胞吞胞吐過程中的囊泡運(yùn)動(dòng)、細(xì)胞遷移等)。但由于所觀察的對(duì)象特征復(fù)雜、運(yùn)動(dòng)模式多變,人工分析海量的TIRFM圖像數(shù)據(jù)不僅十分繁瑣,分析結(jié)果錯(cuò)誤率往往較高。因此,利用計(jì)算機(jī)圖像處理技術(shù)對(duì)顯微圖像進(jìn)行自動(dòng)分析,并提供客觀的定量數(shù)據(jù)幫助量化并驗(yàn)證所觀察到的生命活動(dòng)就顯得尤為重要。細(xì)胞膜附近區(qū)域葡萄糖轉(zhuǎn)運(yùn)蛋白(glucose transporter, Glut)囊泡的運(yùn)動(dòng)情況和微管細(xì)胞骨架的結(jié)構(gòu)形態(tài)與葡萄糖轉(zhuǎn)運(yùn)機(jī)制密切相關(guān)。針對(duì)近膜區(qū)域內(nèi)常見的Glut4囊泡、自噬溶酶體以及微管細(xì)胞骨架等亞細(xì)胞目標(biāo)的TIRFM圖像,本論文開展如下工作：熒光顯微圖像降噪,顯微圖像中特征斑點(diǎn)的檢測,Glut4囊泡與細(xì)胞質(zhì)膜融合過程的自動(dòng)識(shí)別,以及利用多角度TIRF成像技術(shù)重建微管在近膜區(qū)域的三維形態(tài)。論文主要成果及創(chuàng)新點(diǎn)包括：(1)在分析熒光顯微圖像形成的基礎(chǔ)上,提出基于小波多尺度求和(wavelet multiscale addition, WMA)的圖像降噪方法。不同信噪比的模擬圖像實(shí)驗(yàn)結(jié)果表明相對(duì)于線性濾波,高斯平滑濾波以及小波多尺度方差穩(wěn)定變換算法,WMA算法擁有更優(yōu)的降噪效果。WMA算法在C2C12骨骼肌細(xì)胞Glut4囊泡和微管圖像中的降噪效果也得到了驗(yàn)證。(2)建立了根據(jù)熒光斑點(diǎn)的亮度均值和方差構(gòu)建特征空間實(shí)現(xiàn)顯微圖像中的特征斑點(diǎn)檢測(feature particle detection, FPD)算法。模擬圖像實(shí)驗(yàn)結(jié)果表明在信噪比很低(SNR=I)時(shí),FPD算法的準(zhǔn)確檢測率仍能達(dá)到87%。在胸主動(dòng)脈平滑肌細(xì)胞自噬溶酶體的檢測中,FPD算法優(yōu)于ImageJ軟件自帶的Analyze articles (AP)算法,與人工識(shí)別效果的擬合度達(dá)到93%。(3)基于pH值敏感性熒光蛋白VAMP2-pHluorin標(biāo)記Glut4囊泡,應(yīng)用移動(dòng)平均差分算法結(jié)合自適應(yīng)閾值(中值絕對(duì)偏差)能從TIRF圖像序列中檢測出備選Glut4融合囊泡并確定融合起始幀數(shù)。通過對(duì)備選Glut4融合囊泡進(jìn)行逐幀二維Gaussian擬合,得到融合過程中囊泡的參數(shù)變化情況,以此判斷備選Glut4囊泡融合過程屬于完全融合(fullfusion)還是部分融合(partial fusion)。三組已知真實(shí)融合信息的3T3-L1脂肪細(xì)胞TIRF圖像序列用于驗(yàn)證識(shí)別算法的有效性。實(shí)驗(yàn)結(jié)果表明備選Glut4融合囊泡準(zhǔn)確檢測率達(dá)到96.5%,融合過程準(zhǔn)確識(shí)別率達(dá)到84.3%。(4)結(jié)合多角度TIRF顯微技術(shù)得到圖像的更多空間深度信息,擬合不同入射角下激光的透射強(qiáng)度,實(shí)現(xiàn)了觀察目標(biāo)的三維形態(tài)重建。實(shí)驗(yàn)結(jié)果表明在信噪比為2時(shí),探測深度在300nnm范圍內(nèi)的定位精度能達(dá)到40nm。U373細(xì)胞中微管的三維形態(tài)重建結(jié)果與細(xì)胞遷移過程中細(xì)胞骨架的三維空間結(jié)構(gòu)類似,符合生物學(xué)意義。本論文提出的TIRF圖像處理技術(shù)為定量研究細(xì)胞膜附近亞細(xì)胞目標(biāo)的結(jié)構(gòu)形態(tài)和動(dòng)態(tài)生理活動(dòng)提供了客觀的數(shù)據(jù)基礎(chǔ),對(duì)細(xì)胞近膜區(qū)域斑點(diǎn)檢測、融合的動(dòng)態(tài)過程識(shí)別和三維形態(tài)重建具有重要的科學(xué)意義。
[Abstract]:With the progress of optical microscopy, total internal reflection fluorescence microscopy (TIRFM) is able to observe subcellular targets (such as transport vesicles, autophagy) in the area near the thin cell membrane in real time compared with traditional biomedical research methods (such as immunocytochemistry, gene technology, subcellular stratification, etc.) Lysosome, cytoskeleton and so on), and quantitative analysis of its structure and dynamic motion characteristics (such as microtubule structure, vesicle movement in the process of endocytosis, cell migration, etc.). However, because of the complex characteristics of the endocytosis, the movement pattern is changeable, it is not only very tedious to analyze the massive TIRFM image data artificially, but the error rate of the analysis results is often high. Therefore, using computer image processing technology to automatically analyze the microscopic images and provide objective quantitative data to help quantify and verify the observed life activities is particularly important. The movement of glucose transporter (Glut) vesicles in the vicinity of the cell membrane and the structure of the microtubule cytoskeleton It is closely related to the glucose transport mechanism. In view of the TIRFM images of the common Glut4 vesicles, autophagy lysosomes and microtubule cytoskeleton in the near membrane region, the following work is carried out in this paper: fluorescence microscopic image denoising, detection of feature spots in microscopic images, and automatic identification of Glut4 vesicles and cell membrane fusion process The main achievements and innovation points of this paper are as follows: (1) on the basis of the analysis of the formation of fluorescence microscopic images, the image denoising method based on the wavelet multiscale addition (WMA) is proposed. The simulation image experimental junction with different signal to noise ratio is presented. The results show that compared with linear filtering, Gauss smoothing filtering and wavelet multiscale variance stability transform algorithm, the WMA algorithm has better noise reduction effect.WMA algorithm in C2C12 skeletal muscle cells Glut4 vesicles and microtubule image denoising effect is also verified. (2) building characteristic space based on the mean and variance of luminance spots. The feature particle detection (FPD) algorithm is implemented in the microscopic image. The simulation image experimental results show that the accurate detection rate of FPD algorithm can still reach 87%. in the detection of autophagic lysosomes in the thoracic aorta smooth muscle cells when the signal to noise ratio is very low (SNR=I), and FPD algorithm is superior to Analyze articles (AP) with the ImageJ software. The fitting degree of the algorithm and the artificial recognition effect is 93%. (3) based on the pH value sensitive fluorescent protein VAMP2-pHluorin to mark the Glut4 vesicles. The mobile average difference algorithm combined with the adaptive threshold (absolute deviation of the median) can detect the alternative Glut4 fusion vesicles from the TIRF image sequence and determine the number of the initial frames of the fusion. Through the fusion of the alternative Glut4 The vesicles are fitted by two dimensional Gaussian frame by frame, and the parameters of the vesicles in the fusion process are obtained to determine whether the alternative Glut4 vesicle fusion process belongs to the complete fusion (fullfusion) or partial fusion (partial fusion). The three group of 3T3-L1 fat fine cell TIRF images, known as the true fusion information, are used to verify the effectiveness of the recognition algorithm. The experimental results show that the accurate detection rate of the selected Glut4 fusion vesicles reaches 96.5%, the accurate recognition rate of the fusion process reaches 84.3%. (4) and the multi angle TIRF microscopy is used to obtain more spatial depth information of the image, fitting the transmission intensity of the laser under different incident angles and realizing the three-dimensional morphological reconstruction of the observation target. The experimental results show that the signal noise is in the signal to noise. At 2, the location precision of the detection depth in the range of 300nnm can reach the three-dimensional structure of the microtubule in 40nm.U373 cells, which is similar to the three-dimensional spatial structure of the cytoskeleton during the cell migration. The TIRF image processing technique proposed in this paper is a quantitative study of the structure of the subcellular target near the cell membrane. The state and dynamic physiological activities provide an objective data basis. It is of great scientific significance for the detection of the spot in the cell near the membrane, the recognition of the dynamic process of fusion and the reconstruction of the three-dimensional shape.

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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