scholarly journals Trackosome: a computational toolbox to study the spatiotemporal dynamics of centrosomes, nuclear envelope and cellular membrane

2020 ◽  
Author(s):  
Domingos Castro ◽  
Vanessa Nunes ◽  
Joana T. Lima ◽  
Jorge G. Ferreira ◽  
Paulo Aguiar
Keyword(s):  
Nuclear Envelope ◽  
Cellular Membrane ◽  
Spatiotemporal Dynamics ◽  
Imaging Analysis ◽  
Imaging Data ◽  
Analysis Software ◽  
Model Free ◽  
Centrosome Separation ◽  
Efficient Access ◽  
Spatiotemporal Relations

AbstractDuring the initial stages of mitosis, multiple mechanisms drive centrosome separation and positioning. How they are functionally coordinated to promote centrosome migration to opposite sides of the nucleus remains unclear. Imaging analysis software has been used to quantitatively study centrosome dynamics at this stage. However, available tracking tools are generic and not fine-tuned for the constrains and motion dynamics of centrosome pairs. Such generality limits the tracking performance and may require exhaustive optimization of parameters. Here, we present Trackosome, a freely available open-source computational tool to track the centrosomes and reconstruct the nuclear and cellular membranes, based on volumetric live-imaging data. The toolbox runs in MATLAB and provides a graphical user interface for easy and efficient access to the tracking and analysis algorithms. It outputs key metrics describing the spatiotemporal relations between centrosomes, nucleus and cellular membrane. Trackosome can also be used to measure the dynamic fluctuations of the nuclear envelope. A fine description of these fluctuations is important because they are correlated with the mechanical forces exerted on the nucleus by its adjacent cytoskeletal structures. Unlike previous algorithms based on circular/elliptical approximations of the nucleus, Trackosome measures membrane movement in a model-free condition, making it viable for irregularly shaped nuclei. Using Trackosome, we demonstrate significant correlations between the movements of the two centrosomes, and identify specific modes of oscillation of the nuclear envelope. Overall, Trackosome is a powerful tool to help unravel new elements in the spatiotemporal dynamics of subcellular structures.

scholarly journals Trackosome: a computational toolbox to study the spatiotemporal dynamics of centrosomes, nuclear envelope and cellular membrane

2020 ◽  
Vol 133 (24) ◽  
pp. jcs252254
Author(s):  
Domingos Castro ◽  
Vanessa Nunes ◽  
Joana T. Lima ◽  
Jorge G. Ferreira ◽  
Paulo Aguiar
Keyword(s):  
Nuclear Envelope ◽  
Cellular Membrane ◽  
Spatiotemporal Dynamics ◽  
Easy Access ◽  
Source Image ◽  
Imaging Data ◽  
Analysis Software ◽  
Model Free ◽  
Centrosome Separation ◽  
Oscillation Modes

ABSTRACTDuring the initial stages of mitosis, multiple mechanisms drive centrosome separation and positioning. How they are coordinated to promote centrosome migration to opposite sides of the nucleus remains unclear. Here, we present Trackosome, an open-source image analysis software for tracking centrosomes and reconstructing nuclear and cellular membranes, based on volumetric live-imaging data. The toolbox runs in MATLAB and provides a graphical user interface for easy access to the tracking and analysis algorithms. It provides detailed quantification of the spatiotemporal relationships between centrosomes, nuclear envelope and cellular membrane, and can also be used to measure the dynamic fluctuations of the nuclear envelope. These fluctuations are important because they are related to the mechanical forces exerted on the nucleus by its adjacent cytoskeletal structures. Unlike previous algorithms based on circular or elliptical approximations, Trackosome measures membrane movement in a model-free condition, making it viable for irregularly shaped nuclei. Using Trackosome, we demonstrate significant correlations between the movements of the centrosomes, and identify specific oscillation modes of the nuclear envelope. Overall, Trackosome is a powerful tool that can be used to help unravel new elements in the spatiotemporal dynamics of subcellular structures.

scholarly journals Fluorescence Visualization for Oral Mucosa Using an Auto-fluorescence Imaging Analysis Software

2017 ◽  
Vol 30 (2) ◽  
pp. 168-175
Author(s):  
KEISUKE SUGAHARA ◽  
EIKO FUTOO ◽  
HIROKI BESSHO ◽  
RIYO SEKINE ◽  
KEISUKE OHNO ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
Oral Mucosa ◽  
Imaging Analysis ◽  
Analysis Software ◽  
Auto Fluorescence

Functional Imaging Analysis Software — Computational Olio

COMPSTAT ◽  
1996 ◽  
pp. 39-49 ◽  
Author(s):  
William F. Eddy ◽  
Mark Fitzgerald ◽  
Christopher Genovese ◽  
Audris Mockus ◽  
Douglas C. Noll
Keyword(s):  
Functional Imaging ◽  
Imaging Analysis ◽  
Analysis Software

scholarly journals Ultra-fast accurate reconstruction of spiking activity from calcium imaging data

2018 ◽  
Vol 119 (5) ◽  
pp. 1863-1878 ◽  
Author(s):  
Vahid Rahmati ◽  
Knut Kirmse ◽  
Knut Holthoff ◽  
Stefan J. Kiebel
Keyword(s):  
Calcium Imaging ◽  
Reconstruction Method ◽  
Imaging Data ◽  
Decay Kinetics ◽  
Experimental Conditions ◽  
Model Free ◽  
Reconstruction Methods ◽  
Wide Range ◽  
Fast Processing ◽  
Spiking Activity

Calcium imaging provides an indirect observation of the underlying neural dynamics and enables the functional analysis of neuronal populations. However, the recorded fluorescence traces are temporally smeared, thus making the reconstruction of exact spiking activity challenging. Most of the established methods to tackle this issue are limited in dealing with issues such as the variability in the kinetics of fluorescence transients, fast processing of long-term data, high firing rates, and measurement noise. We propose a novel, heuristic reconstruction method to overcome these limitations. By using both synthetic and experimental data, we demonstrate the four main features of this method: 1) it accurately reconstructs both isolated spikes and within-burst spikes, and the spike count per fluorescence transient, from a given noisy fluorescence trace; 2) it performs the reconstruction of a trace extracted from 1,000,000 frames in less than 2 s; 3) it adapts to transients with different rise and decay kinetics or amplitudes, both within and across single neurons; and 4) it has only one key parameter, which we will show can be set in a nearly automatic way to an approximately optimal value. Furthermore, we demonstrate the ability of the method to effectively correct for fast and rather complex, slowly varying drifts as frequently observed in in vivo data. NEW & NOTEWORTHY Reconstruction of spiking activities from calcium imaging data remains challenging. Most of the established reconstruction methods not only have limitations in adapting to systematic variations in the data and fast processing of large amounts of data, but their results also depend on the user’s experience. To overcome these limitations, we present a novel, heuristic model-free-type method that enables an ultra-fast, accurate, near-automatic reconstruction from data recorded under a wide range of experimental conditions.

scholarly journals Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefania Alexandra Iakab ◽  
Lluc Sementé ◽  
María García-Altares ◽  
Xavier Correig ◽  
Pere Ràfols
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Open Source Software ◽  
Multimodal Imaging ◽  
Mass Spectrometry Imaging ◽  
Raman Imaging ◽  
Imaging Analysis ◽  
Imaging Data ◽  
Data Format ◽  
Computational Tools

Abstract Background Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand. Results Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications. Conclusion Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.

Inter-Rater and Intra-Rater Repeatability and Reliability of EOS 3-Dimensional Imaging Analysis Software

2016 ◽  
Vol 31 (5) ◽  
pp. 1091-1095 ◽  
Author(s):  
Alysen L. Demzik ◽  
Hasham M. Alvi ◽  
Dimitri E. Delagrammaticas ◽  
John M. Martell ◽  
Matthew D. Beal ◽  
...  
Keyword(s):  
Imaging Analysis ◽  
Analysis Software ◽  
3 Dimensional

Comparison of two kinds of skin imaging analysis software: VISIA ® from Canfield and IPP ® from Media Cybernetics

2018 ◽  
Vol 24 (3) ◽  
pp. 379-385 ◽  
Author(s):  
X. Wang ◽  
X. Shu ◽  
Z. Li ◽  
W. Huo ◽  
L. Zou ◽  
...  
Keyword(s):  
Imaging Analysis ◽  
Analysis Software ◽  
Skin Imaging

scholarly journals Computerized Quantification of Tissue Vascularization Using High-resolution Slide Scanning of Whole Tumor Sections

2003 ◽  
Vol 51 (2) ◽  
pp. 151-158 ◽  
Author(s):  
Christophe F. Chantrain ◽  
Yves A. DeClerck ◽  
Susan Groshen ◽  
George McNamara
Keyword(s):  
High Resolution ◽  
Blood Vessels ◽  
Random Fields ◽  
Hot Spot ◽  
Mammary Adenocarcinoma ◽  
Imaging Analysis ◽  
Tumor Vascularization ◽  
Analysis Software ◽  
Scanning Method ◽  
Slide Scanner

Assessment of tissue vascularization using immunohistochemical techniques for microvessel detection has been limited by difficulties in generating reproducible quantitative data. The distinction of individual blood vessels and the selection of microscopic fields to be analyzed remain two factors of subjectivity. In this study, we used imaging analysis software and a high-resolution slide scanner for measurement of CD31-immunostained endothelial area (EA) in whole sections of human neuroblastoma xenograft and murine mammary adenocarcinoma tumors. Imaging analysis software provided objective criteria for analysis of sections of different tumors. The use of the criteria on images of entire tumor section acquired with the slide scanner constituted a rapid method to quantify tumor vascularization. Compared with previously described methods, the “hot spot” and the “random fields” methods, EA measurements obtained with our “whole section scanning” method were more reproducible with 8.6% interobserver disagreement for the “whole section scanning” method vs 42.2% and 39.0% interobserver disagreement for the “hot spot” method and the “random fields,” respectively. Microvessel density was also measured with the whole section scanning method and provided additional data on the distribution and the size of the blood vessels. Therefore, this method constitutes a time efficient and reproducible method for quantification of tumor vascularization.

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