- 學位論文
活體細胞中囊泡蛋白質動態特性之自動化定量與定性分析系統
The Automated Quantitative and Qualitative Analysis System for Dynamics of the Proteins of Vesicles in Living Cells
摘要
胞吐作用為蛋白質運輸、神經傳導和激素分泌的重要途徑。藉由先進的顯微和螢光染色的技術，生物學家能於更微小的時間、空間解析度下觀測單一胞吐作用中運輸、定位(docking)、預備(priming)、融合及回收等過程。由於每個細胞中有數百到數千個囊泡，如果沒有輔助工具，生物學家很困難同時對囊泡的特性進行定性判定與量化。 目前文獻中已知的系統僅能提供10-25%囊泡的資訊，導致生物學家無法獲得完整資訊對研究結做出正確的判讀。本研究透過改善影像處理及軌跡追蹤等方法，建構高準確度的自動化螢光囊泡蛋白影像之追蹤輔助系統，全程紀錄囊泡蛋白質的亮度、面積及軌跡變化，使生物學家對其生理機制有完整而正確的判定。針對PC12及βTC3細胞影像中囊泡大小與背景不一，本研究以不同濾波器處理影像雜訊，再採用雙閥值形態學濾波法使系統能正確框選細胞影像大部份大小囊泡區塊。對於移動速度不同的囊泡，本研究中的追蹤法以最短距離連接軌跡或以Kalman filter 輔助預測囊泡位置，最後以階層對應法找到最可能連接之囊泡移動路徑。 由研究結果顯示，以最短距離為追蹤方法的PTrack系統追蹤移動情況少的PC12細胞影像，其正確率比PTrack II多20%。囊泡移動模擬結果中，加入Kalman filter之PTrack II比PTrack增加9~28%追蹤正確率。以βTC3細胞影像驗證追蹤效能，PTrack II之正確率可達56%，比PTrack增加26%。經詳細分析結果後，增加的正確率中大多是速度快的大囊泡，證明PTrack II可以追蹤大面積且移動快速的囊泡。本研究建構的追蹤系統，可以大幅度地提升對囊泡蛋白在活細胞中的動態特質的定性與定量的分析，提供生物學家更完整的資訊。
並列摘要
Membrane trafficking is a very important physiological process involved in protein transport, endocytosis, and exocytosis. The functions of vesicles are strongly correlated with various spatial dynamic properties of vesicles, including their types of movements and morphology. There are hundreds to thousands of synaptic vesicles near the plasma membrane that biologist is diffuclt to capture all the information without any auxiliary tools. The purpose of the research is to help biologist to analyze the dynamic property of the vesicles. This study builds a highly accuracy automated vesicle tracking system by improving the image process and tracking algorighm that records the complete information of vesicle with their physiological mechanism. To identify the vesicle's feature in cell image, the system used different image filters to deal with image noise first, the morphological filter with two-threshold image processing techniques was used to locate granules of various vesicle size in PC12 and βTC3 cells. For those fast-moving and large granules, Kalman filtering was used to improve the performance in tracking process. Performance was evaluated by using five simulations and two cells images (PC12 and βTC3). PTrack got 20% accuracy more than PTrack II after evaluation in PC12 cells images, and the exocytosis event was captured by PTrack exactly. PTrack II was validated using time-lapse images of insulin granules in βTC3 cells, which revealed that PTrack II could track better than PTrack, averaged accuracy up to 56%. The overall tracking results indicate that PTrack get better tracking performance in immobile vesicles, PTrack II is better at tracking vesicles with various dynamic properties, which will facilitate the acquisition of more-complete information on vesicle dynamics.