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研究生:姚瑾芬
研究生(外文):YAOTHAK, JINDAPORN
論文名稱:高爾基管裂變與融合形態和動態之量化分析系統研發
論文名稱(外文):Development of Quantitative System Analysis to Characterize Morphology and Dynamics of Golgi Membrane Tubule Fission and Fusion
指導教授:蔡育秀蔡育秀引用關係
指導教授(外文):TSAI, YUH SHOW
口試委員:江青芬林崇智徐良育蘇振隆
口試委員(外文):JIANG, CHING-FENLIN, CHUNG-CHIHSHYU, LIANG YUSU, JENN LUNG
口試日期:2022-07-07
學位類別:博士
校院名稱:中原大學
系所名稱:生物醫學工程學系
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:英文
論文頁數:103
中文關鍵詞:高爾基體動力學裂變融合分割子類型分類器追踪卡曼濾波器
外文關鍵詞:Golgi dynamicsfissionfusionsegmentationsubtypeclassifiertrackingKaman filter
DOI:10.6840/cycu202301299
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本研究針對真核細胞自我調節機制,特別是涉及活細胞中發生的動態裂變和融合事件的膜小管形成的動力學,開發高爾基膜管裂變與融合之表徵形態及動態的定量分析系統。當前的系統無法準確地描繪和量化膜小管動力學,特別是高爾基體衍生膜的複雜動力學。 缺乏定量體系是導致膜轉運動力學研究進展緩慢的一個重要影響。 因此,本研究的目的是建立一個串接系統,用於表徵和量化高爾基體膜動力學的形態變化和運動,以分析這一重要細胞器的形成和運動的可能模式。
本研究開發兩種不同的目的系統。 第一個系統用於識別高爾基體膜結構並對其進行分型,第二個系統用於追踪高爾基體膜動力學。 與其他方法相比,本研究所提出的分割方法具有最低的均方根誤差。 利用自適應局部歸一化閾值和 Otsu 方法的結合以分割複雜的高爾基管。 從 240 個小管測量的 34 個形態特徵用於訓練分類器,以便從 20 個可能型態中選出最佳分類器。 整灌決策樹的集成分類器類型的最高準確度超過 96%,能夠將六部影片中的所有小管分類為七種形態亞型,即球狀、塊狀、環狀、短狀、中長狀、長狀和分支狀。 高爾基體和小管之間的負相關性表明管狀結構源自高爾基體。 形態亞型的總面積、數量和長度的正相關表明高爾基體來源的膜可能來自共同來源,而負相關表明高爾基體來源的膜可能在亞型之間相互轉化。 為了進一步研究小管相互轉換,我們使用KNN追踪數據影片中的44 個測試的小管,並發現追踪精度超過 94%。 其結果證實,一種腎小管亞型可以通過裂變和融合事件形成另一種涉及長度延長和收縮的亞型。 採用一種改進的固定滯后區間平滑卡爾曼濾波器,可以完全追踪腎小管的暫時消失和前後運動,經模擬電影測試,其追踪精度為100%。 此系統不僅提供十二個運動特徵,還提供軌跡圖,用於快速解釋高爾基體衍生小管的形態進化。
綜合上述,本研究所研發的是第一個表徵高爾基膜小管裂變和融合的形態和動力學的先驅系統。本系統可以幫助生物學家更快、更準確地篩選與這一重要細胞器的形成和運動相關的理解。本系統可以應用線粒體於經歷裂變和融合事件的不同細胞器動力學分析。

Study on self-regulatory mechanisms of eukaryotic cells is not fully understood, and in particular the dynamics of membrane tubule formation involving the dynamic fission and fusion events that occur in living cells. Unfortunately, current systems cannot accurately profile and quantify the membrane tubule dynamics, especially the complicated dynamics of Golgi-derived membranes. The lack of quantitative system is a major impact resulted in slowing down the progress on studying kinetics of membrane transport. Thus, the aim of this study is to establish a pipeline system for characterizing and quantifying morphological changes and movements of Golgi membrane dynamics for profiling possible patterns of formation and motility of this important organelle.
Two main systems presented here were used for different purposes. The first system was to serve identifying and subtyping of Golgi membrane structures, and the second system was to track Golgi membrane dynamics. The proposed segmentation method had the lowest root-mean square error compared to other methods. A combination of adaptive local normalization thresholding and Otsu’s method was excellent segmentation of complex Golgi tubules. Thirty-four morphological features measured from two hundred forty tubules were used for training classifiers to be chosen the best classifier from twenty candidates. The ensemble classifier type of bagged decision trees with highest accuracy of more than 96% was able to classify all tubules from six movies into seven morphological subtypes, i.e. globules, lumps, loops, short, medium length, long, and branch. A negative correlation between Golgi cisternae and tubules revealed that tubular structures are derived from Golgi apparatus. Positive correlations of total area, number, and length of morphological subtypes implied that Golgi-derived membranes may be derived from a common source, while negative correlations indicated that Golgi-derived membranes may interconvert between subtypes. To further investigate the tubule interconversion, forty-four tested tubules were tracked using the k-nearest neighbor well suited for our data movies, and found the tracking accuracy with more than 94%. The tracked results confirmed that one tubule subtype can form to another subtype involving length elongation and shrinking via fission and fusion events. A modified method, fixed-lag interval smoothing Kalman filter, to completely track the tubule temporary disappearance and forward and backward movement, had 100% tracking accuracy with the test of a simulated movie. The tracking system provided not only twelve features of motility, but also track mapping for rapid interpretation of morphological evolution of Golgi-derived tubules.
The pipeline system presented here is the first pioneer system to characterize morphology and dynamics of Golgi membrane tubule fission and fusion. The system can help biologists more quickly and accurately screen for understanding associated with formation and movement of this crucial organelle. Hopefully, this system can be potentially applied to different organelle dynamics undergoing fission and fusion events, e.g. mitochondria.

Content

摘要…………………………………………………………………………………………………………………I
Abstract…………………………………………………………………………………………………………….II
Acknowledgment…………………………………………………………………………………………………III
Content……………………………………………………………………………………………………………IV
List of figures …………………………………………………………………………………………………….IX
List of tables............................................................................................................................................................XI
Chapter 1 Introduction…………………………………………………………………………………………...1
1.1 Background…………………………………………………………………………………………………….1
1.2 Literature review……………………………………………………………………………………………….3
1.2.1 Golgi apparatus and Golgi-derived membrane tubules………………………………………………...3
1.2.1.1 Golgi structure, function, and mechanisms…………………………………………………….3
1.2.1.2 Regulatory mechanisms of Golgi-derived tubule fission and fusion dynamics………………...3
1.2.1.3 Human diseases affected by the protein movements through the Golgi……………………...4
1.2.2 High throughput imaging………………………………………………………………………………4
1.2.3 Basic steps of image processing for dynamic analysis of biological systems…………………………...5
1.2.4 Numerical analysis system for quantification of morphological changes and movements of Golgi membrane dynamics………………………………………………………………………………………….6
1.2.4.1 Survey of segmentation methods and morphological features applying for Golgi apparatus and Golgi-derived membrane tubules………………………………………………………………………8
1.2.4.2 Survey of tracking methods and dynamic features utilized for Golgi membrane dynamics……11
1.2.4.3 Summary of segmentation and tracking methods to be applied for quantification of Golgi membrane dynamics…………………………………………………………………………………..17
1.3 Objectives……………………………………………………………………………………………………..18
1.3.1 Establishing a proper and accurate segmentation, detection, and subtype classification system to characterize key morphological features of Golgi cisternae and Golgi-derived membrane structures……..18
1.3.2 Establishing a suitable tracking system for quantifying dynamic features of the morphological changes and movements of Golgi membrane dynamics……………………………………………………18
1.3.3 Establishing the first prototype of automatic tracking system for covering possible dynamic behaviors through Golgi-derived tubule formation, dynamics, and movements………………………………………19
Chapter 2 Related Methods……………………………………………………………………………………...20
2.1 Adaptive local normalization thresholding…………………………………………………………………...20
2.2 Nearest neighbor search………………………………………………………………………………………21
2.3 Geodesic distance……………………………………………………………………………………………..22
2.4 Kalman filter………………………………………………………………………………………………….23
2.5 Root-mean square error……………………………………………………………………………………….24
2.6 Pearson correlation……………………………………………………………………………………………24
2.7 Violin plot……………………………………………………………………………………………………..25
2.8 Principal component analysis…………………………………………………………………………………25
Chapter 3 Material and Methods……………………………………………………………………………….27
3.1 Live cell imaging of Golgi membrane dynamics……………………………………………………………..27
3.1.1 Time-lapse image data collection of Golgi membrane dynamics……………………………………...27
3.1.2 Characteristics of morphological changes of Golgi membrane dynamics observed from images……..27
3.1.3 Simulated image stack to represent growth movements of Golgi membrane dynamics……………….29
3.2 System design and development……………………………………………………………………………...29
3.2.1 The 2D semi-automated segmentation, detection, and subtype classification system of Golgi cisternae and Golgi-derived membrane structures……………………………………………………………………29
3.2.1.1 Segmentation methods of Golgi cisternae and Golgi-derived membrane structures…………..31
3.2.1.2 Collection of morphological features used for clustering of Golgi-derived membrane structures……………………………………………………………………………………………...31
3.2.1.3 Training of different classifiers based on supervised machine learning algorithms……………………………………………………………………………………………..34
3.2.1.4 Profiling Golgi membrane dynamics using features determined from morphological subtypes……………………………………………………………………………………………….34
3.2.2 The 2D semi-automated tracking system for Golgi membrane images that imaged at high temporal resolution……………………………………………………………………………………………………35
3.2.2.1 Track mapping for visualization……………………………………………………………….37
3.2.2.2 Dynamic features obtained from the first version of tracking system………………………….37
3.2.3 The 2D automatic tracking system of Golgi membrane dynamics with complete conditions in tubule temporary disappearance and forward and backward movements………………………………………….39
3.3 Evaluation methods…………………………………………………………………………………………...47
Chapter 4 Results………………………………………………………………………………………………..48
4.1 Profiling Golgi-derived membrane structures using featured analysis of morphological subtypes obtained from the segmentation and classification system…………………………………………………………………48
4.1.1 System performance of segmentation and classification system……………………………………..48
4.1.1.1 Segmentation performance compared with other algorithms…………………………………48
4.1.1.2 Manual selection and automatic featured clustering of gold standards for trained classifiers..52
4.1.1.3 Classification performance trained by different supervised machine learning algorithms…...53
4.1.1.4 Illustration of segmentation and classification results………………………………………..55
4.1.2 Analysis of features obtained from segmentation and classification system…………………………56
4.1.2.1 Informing which features that should select for training classifiers………………………….56
4.1.2.2 Revealing that Golgi-derived tubules are derived from Golgi cisternae using quantitative features extracted from time-lapse images……………………………………………………………59
4.1.2.3 Suggestion of numerical features contributed to profile morphological subtypes……………60
4.1.2.4 Profiling various Golgi membrane structures using features measured from morphological subtypes……………………………………………………………………………………………….63
4.1.2.5 Supporting results that Golgi-derived tubules may interconversion between different subtypes……………………………………………………………………………………………….64
4.1.3 Summarization of outcomes obtained from the segmentation and classification system…………….65
4.2 Confirmation of interconversion between different subtypes of Golgi tubules by the first version of semi-automated tracking system………………………………………………………………………………………..65
4.2.1 Demonstration of tracking results of Golgi membrane dynamics…………………………………….65
4.2.2 Confirmation of tubule interconversion involved in length elongation and shrinking via fission and fusion events………………………………………………………………………………………………...67
4.2.3 Summarization of outcomes obtained from the first version of semi-automated tracking system…...71
4.3 Characterization of morphological changes during a formation of simulated tubule by the first prototype of automated tracking system………………………………………………………………………………………..72
4.3.1 Performance testing to demonstrate efficiency of the final automatic tracking algorithm…………...72
4.3.2 Characterizing of changes from a simulated tubule movie………………….…………………..........73
4.3.3 Summarization of outcomes obtained from the first prototype of automated tracking system………76
4.4 Summarization of successful study……………………………………………………………………………76
Chapter 5 Discussion…………………………………………………………………………………………….78
5.1 Systems for profiling the Golgi membrane dynamics………………………………………………………...78
5.1.1 Segmentation and subtyping system………………………………………………………………….78
5.1.2 Tracking system……………………………………………………………………………………….78
5.2 Limitations……………………………………………………………………………………………………79
5.2.1 Evaluation of performance of segmentation and tracking methods…………………………………..79
5.2.2 System and software flexibility……………………………………………………………………….79
5.3 Future work…………………………………………………………………………………………………...79
Chapter 6 Conclusion…………………………………………………………………………………………....81
Appendix A……………………………………………………………………………………………………….83
Appendix B……………………………………………………………………………………………………….84
References………………………………………………………………………………………………………..85




List of figures

Fig. 1-1 Basic steps of image processing for biological tracking system……………………………………………6
Fig. 1-2 Comparison of properties of structure and morphology…………………………………………………..7
Fig. 1-3 Effectiveness of adaptive local normalization threshold method for accurate mitochondrial image segmentation………………………………………………………………………………………………………10
Fig. 1-4 Effectiveness of classification using morphological features by MicroP tool…………………………...11
Fig. 1-5 Comparison of ideas for solving the situation of object temporal disappearance……………………….17
Fig. 2-1 Demonstration of finding k-nearest neighbors (k = 5)…………………………………………………..22
Fig. 2-2 Estimation length of original microtubules (a) and by the use of methods, Euclidean vs geodesic (b)…22
Fig. 2-3 Kalman filter operation picture of predict and update states…………………………………………….23
Fig. 2-4 Pearson correlation appeared in scatterplots with positive (a), negative (b), and no (c) correlations…...24
Fig. 2-5 Violin plot using kernel density estimator for visualizing distribution and probability density………...25
Fig. 2-6 Eigenvectors of featured variables of A, B, C, and D (blue lines) analyzed by principal component analysis obtained from normalized data (red dots)……………………………………………………………….26
Fig. 3-1 Morphological changes and dynamics of Golgi membrane structures………………………………….28
Fig. 3-2 Typical morphological changes and movements of Golgi membrane dynamics………………………..28
Fig. 3-3 System diagram of 2D semi-automated system of segmentation, detection, and subtype classification..30
Fig. 3-4 Flowchart of 2D semi-automated tracking system operation……………………………………………36
Fig. 3-5 Trajectory of a tubule movement………………………………………………………………………...39
Fig. 3-6 Highlights of changes in the system diagram of new tracking system…………………………………..40
Fig. 3-7 Tubule searching and matching………………………………………………………………………….41
Fig. 3-8 Concept of fixed-lag interval smoothing filter…………………………………………………………..44
Fig. 3-9 Flowchart of FLIS Kalman filter operation……………………………………………………………...44
Fig. 4-1 Performance of proposed segmentation method compared to other existing methods………………….50
Fig. 4-2 Performance of proposed segmentation method using different levels of noise and intensity………….51
Fig. 4-3 Representative seven clusters of Golgi-derived membrane structures…………………………………..52
Fig. 4-4 Representative results in segmentation and classification of Golgi-derived membranes……………….55
Fig. 4-5 Analysis of useful morphological features calculated from morphological subtypes…………………...58
Fig. 4-6 Indication of why loops and branched tubules can have the same number of branches and tips……….59
Fig. 4-7 Analysis of total area to reveal that Golgi tubules are derived from Golgi cisternae……………………60
Fig. 4-8 Features analysis of Golgi tubule subtypes……………………………………………………………...62
Fig. 4-9 Profiling Golgi dynamics across different time-lapse movies…………………………………………..63
Fig. 4-10 Changes of morphological subtypes through the subsequent frame of Golgi membrane dynamics…...64
Fig. 4-11 Correlation of morphological subtypes of Golgi membrane structures………………………………...65
Fig. 4-12 Representative tracking outcome obtained from 2D semi-automated tracking system………………..66
Fig. 4-13 Analysis of tracking dynamic features…………………………………………………………………69
Fig. 4-14 Movements in 2D and 3D of Golgi tubule dynamics…………………………………………………..70
Fig. 4-15 Tracking outcomes of Golgi tubule dynamics………………………………………………………….71
Fig. 4-16 Demonstration of the 2D automatic tracking algorithm work………………………………………….73
Fig. 4-17 Interpretation of track mapping with track outcome…………………………………………………...75
Fig. 4.18 Overview of the complete system (upper) and successful outcome products (lower) presented in this study………………………………………………………………………………………………………………77
Fig. 6-1 Overview of this study including the stages of problem (a), solution (b), outcome (c), and expected benefits (d)………………………………………………………………………………………………………...82



List of tables

Table 1-1 Survey of segmentation methods and morphological features…………………….................................9
Table 1-2 Survey of tracking methods and dynamic features……………………………….................................13
Table 3-1 Featured selection used as criteria to group Golgi tubule subtypes……………………………...……32
Table 3-2 Morphological features used for classifying subtypes of Golgi-derived membranes…………………32
Table 3-3 Tracking measure of dynamic features for Golgi tubule movements…………….................................38
Table 3-4 Quantitative tracking measures of dynamic features…………………………………………………..46
Table 4-1 Segmentation performance of Golgi membrane structures using different levels of noise and intensity…………………………………………………………………………………………………………...50
Table 4-2 Compares of classification accuracy…………………………………………………………………..53
Table 4-3 Confusion matrix of cross-validation by use of bagged trees…………………………………………54
Table 4-4 The time of blinkout of Golgi cisternae……………………………………………………………….57
Table 4-5 Total of numbers and areas of Golgi cisternae and Golgi-derived membranes……………………….57
Table 4-6 Total of number of Golgi-derived tubules classified into seven morphological subtypes…………….57
Table 4-7 Total of area of Golgi-derived tubules classified into seven morphological subtypes………………...61
Table 4-8 Total of length of Golgi-derived tubules classified into seven morphological subtypes………………61
Table 4-9 Quantification of tracking dynamic measurements……………………………………………………68
Table 4-10 Quantification of tracking dynamic features…………………………………………………………74




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