| 摘要: | 高通量顯微術（High-throughput microscopy, HTM）是癌症研究的一項深具潛力的分析和診斷工具[1]。其特點在於可擷取大面積的影像進而取得於多種生理條件與環境下之巨量的細胞結構以及其行為動力學的時間與空間資訊[2,3]，再經由這些資訊達到無偏見及可系統化的生物過程並進行計算評估[4,5]，並有機會取得傳統生物顯微無法量測與估算的大量或群體細胞生物過程之隱藏特徵。近年來HTM技術在癌症研究方面取得了重大突破，尤其是在篩選抗癌藥物和鑑定耐藥機制方面，但為了建構同時具有大視場（Field of View, FOV）與高分辨率影像的系統，通常需要裝載高倍率顯微鏡與機械掃描裝置組合構建，不僅增加系統的複雜性和成本，同時限制圖像採集速度，因此在一般實驗室環境下仍無法有廣泛使用度。此外，在分析HTM所採集之大量的圖像數據上也是一項艱鉅而耗時的工作，因此在分析多維圖像和自動化特徵提取仍存在迫切的需求[6-9]。本研究項目將發展一種新穎性的HTM方法，稱為深度學習顯微術（Deep Learning Microscopy, DLM）。將採用計算光學與深度學習此兩項新穎技術進行結合，解決當前HTM面臨的技術挑戰。於計算光學系統為使用傅立葉疊層成像顯微術（Fourier ptychographic microscopy, FPM）[10-12]，經由調變不同的照明角度產生具有不同空間移位的光學信息，收集這些低分辨率圖像序列後，重建空間高頻分量之資訊，進而同時取得大視場與高分辨率影像。深度學習（Deep Learning, DL）技術方面，近年來在由大型數據集中獲取與分析信息的能力方面有著令人矚目的進展[13,14]. 尤其在分析高維數據集中複雜的隱藏結構方面成果特別顯著，可有效的使用於高通量影像之分析。本研究計畫將建立一套深度學習顯微系統，利用DL分析來自FPM的大數據圖像資訊，進行癌細胞之篩選抗癌藥物和鑑定耐藥機制之研究[15]。 ;High-throughput microscopy (HTM) is a powerful analytical and diagnostic tool for cancer research[1]. Visualizing a large number of individual cells, HTM generates vast spatiotemporal information on cellular structures and behavioral dynamics under a multitude of physiological conditions[2,3] computationally evaluating the whole aspect of such data could produce unbiased, systematic representations of biological processes[4,5], often unveiling hidden features in bulk or small population measurements. HTM has driven major breakthroughs in cancer research, particularly in screening anti-cancer drugs and identifying resistance mechanisms[6]. Despite HTM’s benefit and potential, technical and practical issues hinder the wide-use of the platform in routine laboratory settings. Conventional microscopes achieve either a large field of-view (FOV) or high resolution, but not both simultaneously. Most HTM systems thus are constructed by combining high-magnification microscopes with mechanically scanning stages. This scheme increase the system complexity and cost, while limiting image acquisition speed. Also, analyzing massive amount of imaging data from HTM is a daunting and labor-intensive. Although recent advances in imaging software have significantly reduced this burden[7-9], further improvements are needed in analyzing multi-dimensional images (e.g., time-lapse movies), and automating feature extraction.This project will advance a new HTM approach, termed deep learning microscopy (DLM). DLM will adopt cutting-edge developments in computational optics and deep learning, addressing technical challenges in the current HTM. Computational optics can overcome fundamental limits of conventional optics by exploiting the power of digital imaging and fast computation. This proposal will specifically use the computational illumination approach, known as Fourier ptychographic microscopy (FPM)[10-12]. FPM collects low resolution image sequences while changing the position of a point-light source; each image has spatially-shifted spectrum information in Fourier space, where the amount of shift depends on the Illumination angle. By numerically stitching image sequences, the Fourier space, including high frequency components, can be reconstructed, which enables FPM to achieve high spatial resolution even with low magnification lenses. Deep learning (DL) is making impressive progress in its ability to derive information from large data sets, to the point where such techniques can outperform human analyses for many data sets[13,14] DL is particularly potent in discovering intricate, hidden structure in high dimensional data sets, and has been adopted for solving quantum physics problem. This proposal will leverage DL power to analyze large images from FPM. We hypothesize that deep learning can extract multidimensional features of individual cells, revealing uncharacterized dynamic cellular processes.We will develop a DLM platform with the capacities for i) high-resolution, wide FOV imaging; ii) molecular profiling on individual cells; and iii) automated imaging analyses (molecular expression, morphology, migration, propagation) on large numbers of single cells. The DLM will transcend the current microscopy. It will have unprecedented analytical power: imaging large number of individual cells at high spatial resolution, and automatically extracting multitude of cellular features. As such, we envision that the DLM will be a transformative tool for cancer research. Potential applications include better monitoring anticancer drug responses, analyzing cellular heterogeneity in large section of tissues, and prospectively detecting cellular fate under various physiological perturbations[15]. |
