We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byKenny Fender
Modified about 1 year ago
RESEARCH POSTER PRESENTATION DESIGN © 2011 www.PosterPresentations.com VISUALIZATION, MODELING, AND SPATIAL STATISTICS OF MITOCHONDRIAL ASSEMBLY IN ADULT CARDIOMYOCYTES USING SERIAL BLOCK-FACE SCANNING ELECTRON MICROSCOPY Mitochondria are recognized as dynamic organelles, which constantly undergo morphological remodeling through fusion and fission processes. However, their geometric details including individual shape, size, and spatial distribution have not been quantitatively characterized in adult cardiomyocytes. Standard optical microscopy is unable to resolve neighboring mitochondria, which are intensely packed between myofilament bundles and narrow sub-sarcolemmal space. On the other hand, 3D reconstructs generated by serial thin- section transmission electron microscopy (EM) or EM tomography can only provide a limited field of view. We approach this problem by using a novel advanced 3D electron microscopic technology, serial block-face scanning electron microscopy (SBFSEM). INTRODUCTION OBJECTIVES Subsequently, we analyzed the spatial distribution of the mitochondrion centroids with a quadrat test, a quantitative measure of intensity (local point density). The cross-sectional cell was divided into smaller regions, and the number of points observed within each quad was compared to the number of points expected in a homogenous Poisson distribution, which assumed complete randomness. This performs a chi-squared test with a null hypothesis of complete statistical random (CSR). STUDY 1: Quadrat Test We performed two trials of distribution analysis using quadrat tests. In the first trial (Fig 3A), cell anatomical features were not taken into consideration. Borderline inhomogeneity with a p-value of 0.053 was found. In the second trial (Fig 3B), to investigate the source of this inhomogeneity, the quadrat test was rerun to incorporate the location of the nucleus in the 3D stack by using the nucleus boundary (shown in Fig 2C) to define anatomy-guided quads. Following the reorganization of quads, the test indicated strong evidence against random distribution and quantitatively proved that mitochondria were clustered near the nucleus (p=0.017). STUDY 2: Quadrat Test STUDY 2: Inter-point Interaction During segmentation of mitochondrion boundaries in cross- sectional slices generated from SBFSEM images, we noticed a distinctly large cluster of mitochondria (Fig. 3A, arrow). A further investigation found the presence of a nucleus right beneath the cluster of mitochondria seen in our original cross- section (Fig 3B). Speculated that the clustering of mitochondria may be due to the close proximity of the nucleus, we then segmented the nucleus boundary and projected onto the original cross-section of segmented mitochondria (Fig 3C). It was evident that the cluster did in fact lie within the shadow of the nucleus. CONCLUSION Distribution analysis of plotted mitochondrion centroids using a quadrat test that incorporated the location of the nucleus in the 3D stack confirmed that mitochondria were clustered near the nucleus (p=0.017). The cell-wide inter-point interaction between mitochondrion centroids calculated by the L-function and the pair correlation function was insufficient to support organized mitochondrial clustering. This may be a result of the single cluster of mitochondria being offset by the homogeneity of mitochondrion distribution in the rest of the cell. Because these statistical analyses do not take into account real world physiological variables such nucleus proximity, the next step would be to integrate these factors into future analyses. The study revealed the strength of the new integrated use of SBFSEM imaging and computational statistics to characterize and parameterize the spatial distribution of cellular organelles such as mitochondria that are dynamically remodeled under a physiological condition and more intensely in disease settings such as diabetic cardiomyopathy and heart failure. ACKNOWLEDGEMENTS Jenny Yan gives special thanks to Elizabeth Theakston of the University of Auckland for her guidance and support during this work. Tapaswani Das is acknowledged for her computational support. UCSD Pacific Rim Undergraduate Experiences (PRIME) made this study possible and we thank to Drs. Peter Arzberger and Gabriele Wienhausen. Jenny Yan was also supported by the Tosh Nomura & Annette Okamoto-Alexander Research Scholarship. SBFSEM imaging was supported by NIH P41 RR004050. Hoshijima was supported by AHA Established Investigator Award. To quantitatively characterize the spatial distribution of cellular organelles such as mitochondria in cardiomyocytes to progress toward a better understanding of the underlying mechanisms of cardiac cell function. Figure 5: Interaction statistics. A, nearest neighbor distances; B, L-function (pair-wise distances), and C, PCF. In (A) and (B) the red line represents the theoretical curve, the black is the observed, and the gray area is the confidence band. In (c), the green represents a Poisson process, the blue is the observed, the black and red are the upper and lower envelopes (confidence band). Adult mouse ventricular tissues were imaged using serial block-face scanning electron microscopy (SBFSEM). Geometric models of mitochondrial organization were created. For quantification, individual mitochondrion boundaries were segmented from the cross-sectional slices obtained from SBFSEM images using software Zinc Digitiser, and the centroid of each boundary was calculated and plotted. Spatial statistical analyses were then applied on the data using the Spatstat package in R (www.spatstat.org) to characterize the distribution of mitochondria. Two studies were performed to investigate the randomness in distribution of centroids: (1) a quadrat test, and (2) inter-point interactions.www.spatstat.org STUDY 2: Inter-point Interaction METHODS 1 University of California San Diego, La Jolla, CA, USA, 2 University of Auckland, Auckland, New Zealand. Jenny Yan 1, Cameron G. Walker 2, Michael J. O’Sullivan 2, Eric A. Bushong 1, Mark H. Ellisman 1, Masahiko Hoshijima 1, Vijayaraghavan Rajagopal 2. The observed interaction was compared to the theoretical interaction if it were a Poisson process. While the nearest neighbor distances were significantly shorter (Fig 5A) and some clustering of points was indicated by L-function analysis (see Fig 5B, the curve partly lies above the theoretical curve), the observed curves of L-function and PCF largely fell within the confidence bands, suggesting a strong trend of spatial distribution homogeneity on mitochondria in ventricular cardiomyocytes. The inter-point interaction of mitochondrion centroids within the cross-section of the cell was also studied. We attempted to determine whether cardiac mitochondria are interactively distributed in ventricular myocytes. Three types of interactions, i.e. no interaction (homogenous/random process; the location of any one point is independent of where another lies), clustering (attraction between points), and regularity (repulsion between points) were characterized by plotting nearest neighbor distances, the L-function, and the pair correlation function (PCF). Nearest neighbor distances: the cumulative distribution function of nearest neighbor distances indicates, on average, the number of points in the point pattern process that have a nearest neighbor within a given radius r. L-function: while nearest neighbor distances only look at one other point, the L-function illustrates how distances between two points (pair-wise distances) change as the neighborhood size changes. For a random Poisson distribution, this change is linear. Thus, if a point process follows a Poisson process, as the neighborhood size increases, pair-wise distances should match that change and increase linearly. PCR: the PCF is a derivative of the L-function and measures the rate of change of pair-wise distances. A B Figure 2. 3D surface-mesh models of mitochondria in adult mouse ventricular cardiomyoyctes generated from SBFSEM images. Part of inter- myofilament mitochondria (purple) were segmented and triangular meshes were generated with that of surface sarcolemma (light blue). The size of this cuboid image is 14 µm x 14 µm x 32 µm (pixel size: 3.5 nm x 3.5 nm, slice interval: 70 nm ). B is a high magnification view of a part of A. Figure 3. Mitochondria boundary determination in cross-sectional slices of a SBFSEM mouse cardiomyocyte image. Cross-section of mouse cardiac cells just above the nucleus (A) and at the necleus (B). Arrows indicate clustered mitochondria. Nuc, nucleus. C shows auto-segmented mitochondrial boundaries generated in the image A with a projected nuclear boundary (shown in a blue line) from the image B. A BC Nuc Figure 4. Quadrat tests. A, original test without anatomical consideration. B, test refinement with the projection of the nuclear boundary to map quads. Figure 1. The work flow of the current study. AB A B C
RESEARCH POSTER PRESENTATION DESIGN © VISUALIZATION, MODELING, AND SPATIAL STATISTICS OF MITOCHONDRIAL ASSEMBLY IN ADULT.
So, what’s the “point” to all of this?….
Methods for point patterns. Methods consider first-order effects (e.g., changes in mean values [intensity] over space) or second-order effects (e.g.,
METU, GGIT 538 CHAPTER V MODELING OF POINT PATTERNS.
Spatial Statistics II RESM 575 Spring 2010 Lecture 8.
Spatial Statistics in Ecology: Point Pattern Analysis Lecture Two.
Point Pattern Analysis Point Patterns fall between the two extremes, highly clustered and highly dispersed. Most tests of point patterns compare the observed.
Analysis of Purified SIV Virions by Cryo- Electron Tomography (A) Low-dose projection image of a plunge- frozen specimen. (B) A series of images recorded.
Statistical Analysis A Quick Overview. The Scientific Method Establishing a hypothesis (idea) Collecting evidence (often in the form of numerical data)
Zakaria A. Khamis GE 2110 GEOGRAPHICAL STATISTICS GE 2110.
Date of download: 7/6/2016 Copyright © 2016 SPIE. All rights reserved. SHG imaging of normal and diseased muscle. Single optical sections of gastrocnemius.
Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. (a) Optical image of fore and hind wings from a male S. charonda butterfly at different.
Date of download: 6/21/2016 Copyright © 2016 SPIE. All rights reserved. Combined two-photon and electrophysiological recording of human neocortical neuronal.
The Examination of Residuals. Examination of Residuals The fitting of models to data is done using an iterative approach. The first step is to fit a simple.
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
Example x y We wish to check for a non zero correlation.
VTSLM images taken again at (a) 4.5 (T=84.7K), (b) 3.85 (T=85.3K), (c) 22.3 (T=85.9K), and (d) 31.6 (T=86.5K) using F-H for current and A-C for.
Final Project : 460 VALLEY CRIMES. Chontanat Suwan Geography 460 : Spatial Analysis Prof. Steven Graves, Ph.D.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 11 Analyzing the Association Between Categorical Variables Section 11.2 Testing Categorical.
1 Chapter 4 – Distance methods Distance sampling (or called plotless sampling) is widely used in forestry and ecology to study the spatial patterns of.
© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
Figure 3. Log-log plot of simulated oscillating phantom, assuming a Gaussian-shaped field. Field constants a 1 =a 2 =0.1. The data initially plateau, then.
The Examination of Residuals. The residuals are defined as the n differences : where is an observation and is the corresponding fitted value obtained.
Horizontal scans were taken every millimeter between the center of the swarm and the end of multiple tendrils using the z-stack function of a Nikon Eclipse.
Figure 8.Color map of the geometric correction along the dispersion axis for segment A. Figure 4. Measured distortions for all PSA positions for segment.
BACKGROUND LEARNING AND LETTER DETECTION USING TEXTURE WITH PRINCIPAL COMPONENT ANALYSIS (PCA) CIS 601 PROJECT SUMIT BASU FALL 2004.
Measures of central tendency are statistics that express the most typical or average scores in a distribution These measures are: The Mode The Median.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Inference on the Least-Squares Regression Model and Multiple Regression 14.
POSTER TEMPLATE BY: Optical Coherence Tomography Assembly of the System and Analysis of Sciatic Rat Nerve Daniel deLahunta.
Course 9 Texture. Definition: Texture is repeating patterns of local variations in image intensity, which is too fine to be distinguished. Texture evokes.
Point Pattern Analysis using Spatial Inferential Statistics Briggs Henan University
The Semivariogram in Remote Sensing: An Introduction P. J. Curran, Remote Sensing of Environment 24: (1988). Presented by Dahl Winters Geog 577,
Intensity Transformations or Translation in Spatial Domain.
COMPUTATIONAL LUNG MODELING GOAL ADVANCING 3D LUNG MODELS To advance 3D airway tree models to predict function from structure particularly when constriction.
Introduction to Mapping Sciences: Lecture #5 (Form and Structure) Form and Structure Describing primary and secondary spatial elements Explanation of spatial.
Quantitative Analysis of Mitochondrial Tubulation Using 3D Imaging Saritha Dwarakapuram*, Badrinath Roysam*, Gang Lin*, Kasturi Mitra§ Department of Electrical.
1 Spatial Statistics and Analysis Methods (for GEOG 104 class). Provided by Dr. An Li, San Diego State University.
MRI Image Segmentation for Brain Injury Quantification Lindsay Kulkin 1 and Bir Bhanu 2 1 Department of Biomedical Engineering, Syracuse University, Syracuse,
Why Is It There? Getting Started with Geographic Information Systems Chapter 6.
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
Issues concerning the interpretation of statistical significance tests.
Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 22 Regression Diagnostics.
SHINE SEP Campaign Events: Long-term development of solar corona in build-up to the SEP events of 21 April 2002 and 24 August 2002 A. J. Coyner, D. Alexander,
Ch 5 Practical Point Pattern Analysis Spatial Stats & Data Analysis by Magdaléna Dohnalová.
Spatial point patterns and Geostatistics an introduction Marian Scott Sept 2006.
III. Results and Discussion In scanning laser microscopy, the detected voltage signal V(x,y) is given by where j b (x,y) is the local current density,
Measuring the Wilson effect: observations and modeling with RHESSI H. Jabran Zahid M. D. Fivian H. S. Hudson.
Biostatistics, statistical software VII. Non-parametric tests: Wilcoxon’s signed rank test, Mann-Whitney U-test, Kruskal- Wallis test, Spearman’ rank correlation.
© 2017 SlidePlayer.com Inc. All rights reserved.