scholarly journals Automated analysis of time-lapse fluorescence microscopy images: from live cell images to intracellular foci

2010 ◽  
Vol 26 (19) ◽  
pp. 2424-2430 ◽  
Author(s):  
O. Dzyubachyk ◽  
J. Essers ◽  
W. A. v. Cappellen ◽  
C. Baldeyron ◽  
A. Inagaki ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Automated Analysis ◽  
Time Lapse ◽  
Live Cell ◽  
Microscopy Images

Automated Analysis of Mitotic Phenotypes in Fluorescence Microscopy Images of Human Cells

2006 ◽  
pp. 374-378 ◽  
Author(s):  
Nathalie Harder ◽  
Beate Neumann ◽  
Michael Held ◽  
Urban Liebel ◽  
Holger Erfle ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Automated Analysis ◽  
Human Cells ◽  
Microscopy Images

scholarly journals Live cell imaging of macrophage/bacterium interaction demonstrates cell lysis induced by Corynebacterium diphtheriae and Corynebacterium ulcerans

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Dulanthi Weerasekera ◽  
Jonas Hahn ◽  
Martin Herrmann ◽  
Andreas Burkovski
Keyword(s):  
Fluorescence Microscopy ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Time Lapse ◽  
Corynebacterium Diphtheriae ◽  
Live Cell ◽  
Human Macrophage ◽  
Data Description ◽  
Corynebacterium Ulcerans ◽  
Microscopy Data

Abstract Objectives In frame of a study to characterize the interaction of human macrophage-like cells with pathogenic corynebacteria, Corynebacterium diphtheriae and Corynebacterium ulcerans, live cell imaging experiments were carried out and time lapse fluorescence microscopy videos were generated, which are presented here. Data description The time lapse fluorescence microscopy data revealed new insights in the interaction of corynebacteria with human macrophage-like THP-1 cells. In contrast to uninfected cells and infections with non-pathogenic C. glutamicum used as a control, pathogenic C. diphtheriae and C. ulcerans showed highly detrimental effects towards human cells and induction of cell death of macrophages.

Non-Gaussian Models for Object Motion Analysis with Time-Lapse Fluorescence Microscopy Images

2021 ◽  
pp. 15-41
Author(s):  
Hanyi Yu ◽  
Sung Bo Yoon ◽  
Robert Kauffman ◽  
Jens Wrammert ◽  
Adam Marcus ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Motion Analysis ◽  
Time Lapse ◽  
Object Motion ◽  
Gaussian Models ◽  
Non Gaussian ◽  
Microscopy Images

Shape reconstruction of subcellular structures from live cell fluorescence microscopy images

2009 ◽  
Vol 167 (1) ◽  
pp. 1-10 ◽  
Author(s):  
J.A. Helmuth ◽  
C.J. Burckhardt ◽  
U.F. Greber ◽  
I.F. Sbalzarini
Keyword(s):  
Fluorescence Microscopy ◽  
Shape Reconstruction ◽  
Live Cell ◽  
Microscopy Images

scholarly journals High-throughput formation and image-based analysis of basal-in mammary organoids in 384-well plates

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Soojung Lee ◽  
Jonathan Chang ◽  
Sung-Min Kang ◽  
Eric Parigoris ◽  
Ji-Hoon Lee ◽  
...  
Keyword(s):  
Growth Factor ◽  
High Throughput ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Automated Analysis ◽  
Time Lapse ◽  
Culture Conditions ◽  
Live Cell ◽  
Future Research ◽  
Heparin Binding

AbstractThis manuscript describes a new method for forming basal-in MCF10A organoids using commercial 384-well ultra-low attachment (ULA) microplates and the development of associated live-cell imaging and automated analysis protocols. The use of a commercial 384-well ULA platform makes this method more broadly accessible than previously reported hanging drop systems and enables in-incubator automated imaging. Therefore, time points can be captured on a more frequent basis to improve tracking of early organoid formation and growth. However, one major challenge of live-cell imaging in multi-well plates is the rapid accumulation of large numbers of images. In this paper, an automated MATLAB script to handle the increased image load is developed. This analysis protocol utilizes morphological image processing to identify cellular structures within each image and quantify their circularity and size. Using this script, time-lapse images of aggregating and non-aggregating culture conditions are analyzed to profile early changes in size and circularity. Moreover, this high-throughput platform is applied to widely screen concentration combinations of Matrigel and epidermal growth factor (EGF) or heparin-binding EGF-like growth factor (HB-EGF) for their impact on organoid formation. These results can serve as a practical resource, guiding future research with basal-in MCF10A organoids.

Morphology And Status Transition Monitoring For Mouse Embryonic Stem Cell Colonies In Virto By LSTM Networks With Progressive Training Using Fluorescence Microscopy Images

2021 ◽  
Author(s):  
Slo-Li Chu ◽  
Kuniya Abe ◽  
Hideo Yokota ◽  
Ming-Dar Tsai
Keyword(s):  
Fluorescence Microscopy ◽  
Es Cells ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cell ◽  
Time Lapse ◽  
Short Term ◽  
Status Transition ◽  
Biological Studies ◽  
Microscopy Images ◽  
Morphology Changes

Abstract Purpose Embryonic stem (ES) cells represent as a cellular resource for basic biological studies and for their uses as medically relevant cells in in vitro studies. Fluorescence microscopy images taken during cell culture are frequently used to manually monitor time-series morphology changes and status transitions of ES cell (ESC) colonies, and to study dynamical pattern formation and heterogeneity distribution within ESC colonies, intrinsic fluctuation and cell-cell cooperativity. Therefore, tracking and furthermore predicting morphology changes and status transitions of ESC colonies is an effective method to monitor culture medium for maintaining ES cells in undifferentiated or early differentiated stage. Methods A P-LSTM (Progressive Long Short-Term Memory) structure is proposed to incorporate some new time-lapse images real-time taken from incubators for a new RNN (Recurrent Neural Networks) training. The P-LSTM can achieve adaptive long- and short- term memories to generate accurate predicted images. On the time-lapse images, entropy and bi-lateral filtering are used to extract the range of every colony to calculate colony morphology. Colony status transitions between consecutive images are calculated by mapping the calculated colony centers and ranges. Results Accuracies for the colony status transition, area and roundness for the 15 predicted (five-hour) future frames calculated from 1500-2500 colonies for respective frames show the effectiveness of the proposed method.Conclusion We proposed an efficient and automatic method to predict and monitor status transitions and morphology changes of mouse ESC colonies in culture using time-lapse fluorescence microscopy images.

scholarly journals Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

2006 ◽  
Vol 2006 ◽  
pp. 1-10
Author(s):  
S. Venkatraman ◽  
M. J. Doktycz ◽  
H. Qi ◽  
J. L. Morrell-Falvey
Keyword(s):  
Fluorescence Microscopy ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Networks ◽  
Automated Analysis ◽  
Automated System ◽  
Positive Interactions ◽  
Protein Interaction Dataset ◽  
Analysis System ◽  
Microscopy Images

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.

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