具有雙螺旋點擴散函數的 3D 單分子定位顯微鏡實時定位

Abstract

自超高分辨率顯微鏡（SRM）出現以來已發展了許多方法，使研究人員能透過遠超傳統光學顯微鏡的解析度在奈米尺度上研究生物細胞的分子結構，解析度遠超過傳統光學顯微鏡(約200 nm)。 在SRM領域中，單分子定位顯微鏡（SMLM）技術受到多數研究家的廣泛關注，因為它具備卓越的亞像素空間分辨率，能夠以低於5奈米的精度觀察整個細胞結構。此外雙螺旋點擴散函數是SMLM在3D研究上的重要工具，它允許我們能以高度精確的方式重建三維空間中的細胞結構，這項技術為深入探索細胞內部的分子組織和互動提供了新的可能性。 由於SMLM技術要精確定位所有單分子位置與重建圖像結構，當中通常涉及龐大的數據計算與處理工作以獲得最終的高解析度成像，因此有必要使用高效演算法來優化程式。 通過結合SMLM與Point Spread Function 的Double-Helix模型開發了一套程式碼，旨在獲取特定分子結構並精確定位每幀中熒光分子的位置。這套程式使用C++ QT Creator作為開發介面，結合PCO.EDGE科學相機作為檢測生物細胞的分析平台，其中涉及數十萬組的點雲數據計算則交由圖形處理器(Graphics Processing Unit)執行並行高效處理，實現快速且準確的評估。我的程式還能夠同時觀看細胞實時影像與超解析成像的擬合過程。

Since the advent of super-resolution microscopy (SRM), a multitude of techniques have emerged, enabling researchers to investigate the molecular structure of biological cells at the nanometer scale with resolutions far surpassing those achievable with traditional optical microscope (Resolution is approximately 200 nm). In the realm of super-resolution microscopy studies, single-molecule localization microscopy (SMLM) technology holds a prominent position among researchers. Its appeal lies in its ability to provide an extraordinary spatial resolution of less than 5 nanometers, enabling the observation of entire cellular structures in unprecedented detail. The Double-Helix point spread function plays a pivotal role in 3D SMLM research, facilitating the precise reconstruction of cellular structures in three-dimensional space. This groundbreaking technology opens up new avenues for in-depth exploration of molecular organization and interactions within cells. However, the demands of accurately locating the positions of individual molecules and reconstructing image structures in SMLM entail extensive data calculation and processing. As a result, efficient algorithms are indispensable to optimize computational resources. Here, I developed a set of codes that specifically target molecular structures and enable precise localization of individual fluorescent molecules. My program utilizes C++ QT Creator as the development interface and leverages the PCO.EDGE scientific camera as an analysis platform for cellular tissue observation. The processing of hundreds of thousands of sets of point cloud data is efficiently carried out in parallel by Graphics Processing Unit (GPU), enabling rapid and precise evaluation. My program can also observe the real-time cell imaging and super-resolution imaging processes simultaneously.