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Automated analysis of cell-matrix adhesions in 2D and 3D environments.

Broussard JA, Diggins NL, Hummel S, Georgescu W, Quaranta V, Webb DJ - Sci Rep (2015)

Bottom Line: Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis.To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation.PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences and Vanderbilt Kennedy Center for Research on Human Development, Vanderbilt University, Nashville, Tennessee 37235.

ABSTRACT
Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis. Adhesions are dynamic structures that are classically defined on two-dimensional (2D) substrates, though the need to analyze adhesions in more physiologic three-dimensional (3D) environments is being increasingly recognized. However, progress has been greatly hampered by the lack of available tools to analyze adhesions in 3D environments. To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation. PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates. We manually validate PAASTA and utilize it to quantify rate constants for adhesion assembly and disassembly as well as adhesion lifetime and size in 3D matrices. PAASTA will be a valuable tool for characterizing adhesions and for deciphering the molecular mechanisms that regulate adhesion dynamics in 3D environments.

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Related in: MedlinePlus

Adhesion identification and tracking using PAASTA.Raw time-lapse TIRF images of an HT1080 cell expressing GFP-paxillin are shown (upper panels). These images were then processed with PAASTA to generate individual adhesion tracks that are shown in the lower panels both without labeling (No ID numbers) and with ID numbers labeling each adhesion (With ID numbers). Bar, 5 μm.
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f2: Adhesion identification and tracking using PAASTA.Raw time-lapse TIRF images of an HT1080 cell expressing GFP-paxillin are shown (upper panels). These images were then processed with PAASTA to generate individual adhesion tracks that are shown in the lower panels both without labeling (No ID numbers) and with ID numbers labeling each adhesion (With ID numbers). Bar, 5 μm.

Mentions: In order to perform automated detection and quantification of adhesions over time, we begin with raw images, which are acquired with time-lapse microscopy, of cells with fluorescently-labeled adhesions (Fig. 1). Initially, a Gaussian smoothing module is applied to the raw images to reduce noise and then corrected for uneven background illumination by dividing each smoothed image with a low pass filtered version of itself. To detect adhesions, we employ a local thresholding module that compares the intensity of each pixel with the mean value of the local neighborhood of the pixel. If the value of the pixel is higher than the local average, it is classified as an adhesion pixel; otherwise, it is assigned to the background pixel class. Objects less than 1 μm2 are excluded to ensure that background noise is eliminated from the analysis. Cell outlines are detected by thresholding the background-corrected images using a global intensity threshold module. Adhesions are selected by combining the binary mask of the cell with the binary image of the adhesions. Individual adhesions, which are assigned identification (ID) numbers, are tracked over time using a nearest neighbor algorithm. Adhesion ID numbers, individual adhesion integrated intensities, and area information at every time point are exported to comma-separated text files for further analysis. Sets of images showing the detected adhesion outlines, with or without ID numbers, are overlaid on the original images for manual validation of the automated quantification (Fig. 2).


Automated analysis of cell-matrix adhesions in 2D and 3D environments.

Broussard JA, Diggins NL, Hummel S, Georgescu W, Quaranta V, Webb DJ - Sci Rep (2015)

Adhesion identification and tracking using PAASTA.Raw time-lapse TIRF images of an HT1080 cell expressing GFP-paxillin are shown (upper panels). These images were then processed with PAASTA to generate individual adhesion tracks that are shown in the lower panels both without labeling (No ID numbers) and with ID numbers labeling each adhesion (With ID numbers). Bar, 5 μm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4309964&req=5

f2: Adhesion identification and tracking using PAASTA.Raw time-lapse TIRF images of an HT1080 cell expressing GFP-paxillin are shown (upper panels). These images were then processed with PAASTA to generate individual adhesion tracks that are shown in the lower panels both without labeling (No ID numbers) and with ID numbers labeling each adhesion (With ID numbers). Bar, 5 μm.
Mentions: In order to perform automated detection and quantification of adhesions over time, we begin with raw images, which are acquired with time-lapse microscopy, of cells with fluorescently-labeled adhesions (Fig. 1). Initially, a Gaussian smoothing module is applied to the raw images to reduce noise and then corrected for uneven background illumination by dividing each smoothed image with a low pass filtered version of itself. To detect adhesions, we employ a local thresholding module that compares the intensity of each pixel with the mean value of the local neighborhood of the pixel. If the value of the pixel is higher than the local average, it is classified as an adhesion pixel; otherwise, it is assigned to the background pixel class. Objects less than 1 μm2 are excluded to ensure that background noise is eliminated from the analysis. Cell outlines are detected by thresholding the background-corrected images using a global intensity threshold module. Adhesions are selected by combining the binary mask of the cell with the binary image of the adhesions. Individual adhesions, which are assigned identification (ID) numbers, are tracked over time using a nearest neighbor algorithm. Adhesion ID numbers, individual adhesion integrated intensities, and area information at every time point are exported to comma-separated text files for further analysis. Sets of images showing the detected adhesion outlines, with or without ID numbers, are overlaid on the original images for manual validation of the automated quantification (Fig. 2).

Bottom Line: Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis.To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation.PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences and Vanderbilt Kennedy Center for Research on Human Development, Vanderbilt University, Nashville, Tennessee 37235.

ABSTRACT
Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis. Adhesions are dynamic structures that are classically defined on two-dimensional (2D) substrates, though the need to analyze adhesions in more physiologic three-dimensional (3D) environments is being increasingly recognized. However, progress has been greatly hampered by the lack of available tools to analyze adhesions in 3D environments. To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation. PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates. We manually validate PAASTA and utilize it to quantify rate constants for adhesion assembly and disassembly as well as adhesion lifetime and size in 3D matrices. PAASTA will be a valuable tool for characterizing adhesions and for deciphering the molecular mechanisms that regulate adhesion dynamics in 3D environments.

Show MeSH
Related in: MedlinePlus