PowerPoint - Fluorescence Bioimaging of Organellar Network Evolution (Pomona)
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Fluorescence Bioimaging of Organellar Network Evolution Chinasa T. Okolo 1 The University of Georgia – Departments of Computer Science and Infectious Diseases 1 Pomona College, Claremont, CA 91711 Methods Results References Next Steps Introduction Years of meticulous work have led to a comprehensive understanding of the functions of subcellular organelles. However, there have been severe limitations to the approaches that can be implemented to execute these studies, therefore inhibiting a complete understanding of the effects of complex stimuli on cell function. There are a significant number of bioimaging toolkits available, but currently there are no bioimaging application models with the capability to tag organelles as dynamic social networks. Being able to model these patterns as evolving networks with respect to time will yield additional insights that have not been possible with current bioimaging methods. The analytical framework designed in this project was implemented using open source tools, which will help increase the accessibility of these tools and the transparency of future analyses. This will simplify the collaborative development processes for future iterations of the software while also providing tools other researchers can readily adapt to similar problems. This project highlighted the preliminary steps needed for designing and implementing a high- throughput, large-scale computational pipeline to detect changes in the organellar morphology of cells. Listeria monocytogenes was used as a model within this project, but to characterize the spatio- temporal evolution of organellar networks, the next step of this project will use cells infected with Mycobacterium tuberculosis and Legionella pneumophila, respectively. The completed framework will quantify changes in organellar shape, quantity, and spatial distribution over large sequences of Z-stack microscope images and digital videos, which will improve Mahotas, scikit-image, ImageJ, MATLAB, and scipy were implemented to design the analysis framework. NIS Elements Viewer was used to separate each Z-stack into Grayscale images of Alx594 and DAPI channels. The DAPI image file was then reedited with ImageJ to show a higher level of fluorescence within the cells and the “Cell Counter” tool was used to count the cells within the image. Next, the reedited DAPI file was imported into the Python environment to be segmented using Gaussian filtering of one standard deviation. In an effort to watershed the area of clumped cells within each DAPI image, the non-clumped cells were thresholded out. The first thresholded image of clumped cells was imported into ImageJ to find the perimeter, centroid coordinates, and area of each section of clumped cells. The DAPI images were also imported into MATLAB to produce a visual output of the centroid locations. After this, a thresholded image of the original DAPI file was created in the Python environment and imported into ImageJ. This image was filtered using the watershed function and was further analyzed to find the area, perimeter, and centroid coordinates of the watershed cells. Acknowledgements Actin Staining Protocol. Actin Staining Protocol | Life Technologies Coelho, Luis Pedro. Labeled Image Functions — Mahotas 1.4.0 Documentation. Labeled Image Functions. Mahotas, (2015). Jammalamadaka, A. et al. Characterizing spatial distributions of astrocytes in the mammalian retina. Bioinformatics btv097, (2015). Li, J. et al. Automated Analysis and Reannotation of Subcellular Locations in Confocal Images from the Human Protein Atlas. PLoS One 7, (2012). The MathWorks, Inc. Documentation - Regionprops. Measure Properties of Image Regions. (2015). Pythonvision.org. Python Image Tutorial. Tutorial. (2015). This research was made possible by the NSF-funded Fungal Genomics and Computational Biology REU program at the University of Georgia. I would like to thank my PI, Dr. Shannon Quinn and Dr. Pramod Giri, Jamie Barber, and Shelly Helms for their valuable assistance with this project. 6 hours uninfected 6 hours infected 24 hours uninfected 24 hours infected 245 cells Area X Y Perim Mean 1704.731 517.112 531.789 157.552 SD 1433.288 308.152 304.165 67.169 Min 160 7.429 6.21 47.899 Max 15574 1020.285 1018.4 625.404 291 cells Area X Y Perim Mean 310.515 237.352 229.684 62.275 SD 674.394 139.651 149.919 55.034 Min 1 7.325 0.9 2.828 Max 9110 479.5 478.1 655.855 343 cells Area X Y Perim Mean 344.930 244.735 241.166 68.739 SD 454.540 141.369 142.013 44.661 Min 6 1.5 1.346 7.657 Max 4167 479 476.205 348.309 408 cells Area X Y Perim Mean 358.0515 251.3530 256.0952 69.8664 SD 394.156 137.719 141.401 45.096 Min 3 2.522 1.5 8.485 Max 4008 478.5 477.5 361.061
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Transcript of PowerPoint - Fluorescence Bioimaging of Organellar Network Evolution (Pomona)
Fluorescence Bioimaging of Organellar Network Evolution
Chinasa T. Okolo1
The University of Georgia – Departments of Computer Science and Infectious Diseases
1Pomona College, Claremont, CA 91711
MethodsResults
References
Next Steps
Introduction
Years of meticulous work have led to a comprehensive understanding of the functions of subcellular organelles. However, there have been severe limitations to the approaches that can be implemented to execute these studies, therefore inhibiting a complete understanding of the effects of complex stimuli on cell function. There are a significant number of bioimaging toolkits available, but currently there are no bioimaging application models with the capability to tag organelles as dynamic social networks. Being able to model these patterns as evolving networks with respect to time will yield additional insights that have not been possible with current bioimaging methods.
The analytical framework designed in this project was implemented using open source tools, which will help increase the accessibility of these tools and the transparency of future analyses. This will simplify the collaborative development processes for future iterations of the software while also providing tools other researchers can readily adapt to similar problems.
This project highlighted the preliminary steps needed for designing and implementing a high-throughput, large-scale computational pipeline to detect changes in the organellar morphology of cells. Listeria monocytogenes was used as a model within this project, but to characterize the spatio-temporal evolution of organellar networks, the next step of this project will use cells infected with Mycobacterium tuberculosis and Legionella pneumophila, respectively.
The completed framework will quantify changes in organellar shape, quantity, and spatial distribution over large sequences of Z-stack microscope images and digital videos, which will improve our understanding of cellular mechanisms as they respond to their environments.
Mahotas, scikit-image, ImageJ, MATLAB, and scipy were implemented to design the analysis framework. NIS Elements Viewer was used to separate each Z-stack into Grayscale images of Alx594 and DAPI channels. The DAPI image file was then reedited with ImageJ to show a higher level of fluorescence within the cells and the “Cell Counter” tool was used to count the cells within the image.
Next, the reedited DAPI file was imported into the Python environment to be segmented using Gaussian filtering of one standard deviation. In an effort to watershed the area of clumped cells within each DAPI image, the non-clumped cells were thresholded out. The first thresholded image of clumped cells was imported into ImageJ to find the perimeter, centroid coordinates, and area of each section of clumped cells.
The DAPI images were also imported into MATLAB to produce a visual output of the centroid locations. After this, a thresholded image of the original DAPI file was created in the Python environment and imported into ImageJ. This image was filtered using the watershed function and was further analyzed to find the area, perimeter, and centroid coordinates of the watershed cells.
Acknowledgements
Actin Staining Protocol. Actin Staining Protocol | Life Technologies Coelho, Luis Pedro. Labeled Image Functions — Mahotas 1.4.0 Documentation. Labeled Image Functions. Mahotas, (2015). Jammalamadaka, A. et al. Characterizing spatial distributions of astrocytes in the mammalian retina. Bioinformatics btv097, (2015). Li, J. et al. Automated Analysis and Reannotation of Subcellular Locations in Confocal Images from the Human Protein Atlas. PLoS One 7, (2012).The MathWorks, Inc. Documentation - Regionprops. Measure Properties of Image Regions. (2015). Pythonvision.org. Python Image Tutorial. Tutorial. (2015).
This research was made possible by the NSF-funded Fungal Genomics
and Computational Biology REU program at the University of Georgia. I would like to thank my PI, Dr. Shannon
Quinn and Dr. Pramod Giri, Jamie Barber, and Shelly Helms for their
valuable assistance with this project.
6 hours uninfected 6 hours infected
24 hours uninfected 24 hours infected
245 cells Area X Y Perim
Mean 1704.731 517.112 531.789 157.552
SD 1433.288 308.152 304.165 67.169
Min 160 7.429 6.21 47.899
Max 15574 1020.285 1018.4 625.404
291 cells Area X Y Perim
Mean 310.515 237.352 229.684 62.275
SD 674.394 139.651 149.919 55.034
Min 1 7.325 0.9 2.828
Max 9110 479.5 478.1 655.855
343 cells Area X Y Perim
Mean 344.930 244.735 241.166 68.739
SD 454.540 141.369 142.013 44.661
Min 6 1.5 1.346 7.657
Max 4167 479 476.205 348.309
408 cells Area X Y Perim
Mean 358.0515 251.3530 256.0952 69.8664
SD 394.156 137.719 141.401 45.096
Min 3 2.522 1.5 8.485
Max 4008 478.5 477.5 361.061
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