Details

Name
Tube Formation Assay
IQbot ID
IQBOT-0001
Version
1.0.0
Research Areas
Angiogenesis & Cancer Research
Processing Time Expectation
Less than 4 Minutes per Image
Acquisition Hardware
Microscope
Techniques, Dyes, Stains, & Proteins
Phase Contrast
Sample Context
in-vitro
References
1.   Tan K, Lessieur E, Cutler A, Nerone P, Vasanji A, Asosingh K, Erzurum S, Anand-Apte B. Impaired function of circulating CD34(+) CD45(-) cells in patients with proliferative diabetic retinopathy. Exp Eye Res. 2010 Aug;91(2):229-37. doi: 10.1016/j.exer.2010.05.012. Epub 2010 May 21. PubMed PMID: 20493838; PubMed Central PMCID: PMC3895932.
2.   Asosingh K, Aldred MA, Vasanji A, Drazba J, Sharp J, Farver C, Comhair SA, Xu W, Licina L, Huang L, Anand-Apte B, Yoder MC, Tuder RM, Erzurum SC. Circulating angiogenic precursors in idiopathic pulmonary arterial hypertension. Am J Pathol. 2008 Mar;172(3):615-27. doi: 10.2353/ajpath.2008.070705. Epub 2008 Feb 7. PubMed PMID: 18258847; PubMed entral PMCID: PMC2258264.
3.   Masri FA, Xu W, Comhair SA, Asosingh K, Koo M, Vasanji A, Drazba J, Anand-ApteB, Erzurum SC. Hyperproliferative apoptosis-resistant endothelial cells in idiopathic pulmonary arterial hypertension. Am J Physiol Lung Cell Mol Physiol. 2007 Sep;293(3):L548-54. Epub 2007 May 25. PubMed PMID: 17526595.
4.   West XZ, Meller N, Malinin NL, Deshmukh L, Meller J, Mahabeleshwar GH, Weber ME, Kerr BA, Vinogradova O, Byzova TV. Integrin β3 crosstalk with VEGFR accommodating tyrosine phosphorylation as a regulatory switch. PLoS One. 2012;7(2):e31071. doi: 10.1371/journal.pone.0031071. Epub 2012 Feb 17. PubMed PMID: 22363548; PubMed Central PMCID: PMC3281915.
Name
Tube Formation Assay
IQbot ID
IQBOT-0001
Version
1.0.0
Research Areas
Angiogenesis & Cancer Research
Processing Time Expectation
Less than 4 Minutes per Image
Acquisition Hardware
Microscope
Techniques, Dyes, Stains, & Proteins
Phase Contrast
Sample Context
in-vitro
References
1.   Tan K, Lessieur E, Cutler A, Nerone P, Vasanji A, Asosingh K, Erzurum S, Anand-Apte B. Impaired function of circulating CD34(+) CD45(-) cells in patients with proliferative diabetic retinopathy. Exp Eye Res. 2010 Aug;91(2):229-37. doi: 10.1016/j.exer.2010.05.012. Epub 2010 May 21. PubMed PMID: 20493838; PubMed Central PMCID: PMC3895932.
2.   Asosingh K, Aldred MA, Vasanji A, Drazba J, Sharp J, Farver C, Comhair SA, Xu W, Licina L, Huang L, Anand-Apte B, Yoder MC, Tuder RM, Erzurum SC. Circulating angiogenic precursors in idiopathic pulmonary arterial hypertension. Am J Pathol. 2008 Mar;172(3):615-27. doi: 10.2353/ajpath.2008.070705. Epub 2008 Feb 7. PubMed PMID: 18258847; PubMed entral PMCID: PMC2258264.
3.   Masri FA, Xu W, Comhair SA, Asosingh K, Koo M, Vasanji A, Drazba J, Anand-ApteB, Erzurum SC. Hyperproliferative apoptosis-resistant endothelial cells in idiopathic pulmonary arterial hypertension. Am J Physiol Lung Cell Mol Physiol. 2007 Sep;293(3):L548-54. Epub 2007 May 25. PubMed PMID: 17526595.
4.   West XZ, Meller N, Malinin NL, Deshmukh L, Meller J, Mahabeleshwar GH, Weber ME, Kerr BA, Vinogradova O, Byzova TV. Integrin β3 crosstalk with VEGFR accommodating tyrosine phosphorylation as a regulatory switch. PLoS One. 2012;7(2):e31071. doi: 10.1371/journal.pone.0031071. Epub 2012 Feb 17. PubMed PMID: 22363548; PubMed Central PMCID: PMC3281915.

Output Parameters

Vessel Area (pixels) 
The total area, measured in pixels, of the vessels shown in each image
Total Node Count
The number of nodes or branching points between branches within each image
3 Branch Node Count
The number of nodes with exactly 3 emanating branche
4 Branch Node Count
The number of nodes with exactly 4 emanating branches
5+ Branch Node Count
The number of nodes with 5 or more emanating branches
Skeletal Length (pixels)
The total length of the vasculature, measured in pixels, in each image
Mean Node Thickness (pixels)
The average thickness, measured in pixels, of the nodes in each image
Standard Deviation Node Thickness (pixels) 
The standard deviation, measured in pixels, of the nodes in each image
Branch Count
The total number of branches within the tubular network in each image
Mean Branch Thickness (pixels) 
The average thickness, measured in pixels, of the branches in each image
Standard Deviation Branch Thickness (pixels) 
The standard deviation, measured in pixels, of the branches in each image
Lumen Count 
The number of observed bounded lumens formed in each image
Mean Lumen Area (pixels) 
The average area, measured in pixels, of the lumens formed in each image

 

This section will identify any output parameters that are only output by the IQbot in its advanced configuration.

 

Vessel Area (pixels) 
The total area, measured in pixels, of the vessels shown in each image
Total Node Count
The number of nodes or branching points between branches within each image
3 Branch Node Count
The number of nodes with exactly 3 emanating branche
4 Branch Node Count
The number of nodes with exactly 4 emanating branches
5+ Branch Node Count
The number of nodes with 5 or more emanating branches
Skeletal Length (pixels)
The total length of the vasculature, measured in pixels, in each image
Mean Node Thickness (pixels)
The average thickness, measured in pixels, of the nodes in each image
Standard Deviation Node Thickness (pixels) 
The standard deviation, measured in pixels, of the nodes in each image
Branch Count
The total number of branches within the tubular network in each image
Mean Branch Thickness (pixels) 
The average thickness, measured in pixels, of the branches in each image
Standard Deviation Branch Thickness (pixels) 
The standard deviation, measured in pixels, of the branches in each image
Lumen Count 
The number of observed bounded lumens formed in each image
Mean Lumen Area (pixels) 
The average area, measured in pixels, of the lumens formed in each image
Endpoint Count
The number of terminal endpoints in each image
Mean Node-to-Node Length (pixels) 
The average length, measured in pixels, between two connected nodes in each image
Mean Endpoint-to-Node Length (pixels) 
The average length, measured in pixels, between a node and connected terminal endpoint in each image

Image Acquisition


This IQbot was initially developed for image sets utilizing Phase Contrast and Fluorescent 2D Microscopy to track the formation of tubes in an extracellular matrix mimicking a basement membrane. To accurately analyze assay cells grown in wells Large Field of View Imaging was employed, which requires a wide-field microscope fitted with a motorized stage. This hardware enables imaging scientists to scan across each well and capture the entire network by stitching together high magnification image tiles acquired in either Brightfield or Fluorescence mode. The result is a very large, high-resolution image that provides the fine detail necessary for accurate image segmentation.

Since its original deployment, the IQbot’s algorithms and filters have been subjected to a diverse set of tube formation images. Due to the continuous tweaking and improving of the analysis with each supplemental image set, the IQbot presented to you and available through this site is an extremely robust software tool. Its use history has also allowed for the development of alternative IQbots that perform the same analyses on images acquired with Brightfield Microscopy or using GFP or Dil. If you have need of those or related IQbots, please reach out to the ImageQuantify.com support team via the contact information posted at the bottom of this page.

 
 

The Tube Formation Assay IQbot has been built to accommodate the following imaging types only.

   .tif 
  .tiff
  .png
 .jpg
.jpeg
 
 

As we reference for IQbots in general here, the Tube Formation Assay analysis is only ultimately effective if you upload high quality images for processing. Please ensure that any images you upload follow the below guidelines specific to this IQbot.

High-Resolution:  The quality of the output data you receive is largely dependent on the input image resolution. Please provide high-resolution images to ensure that the IQbot can most effectively differentiate between tubes and the image background.
In-Focus:  Please produce the most in-focus images possible for analysis. Images with high background contrast will result in more accurate and useful data and overlays.
Properly Saturated:  If your submitted images are significantly undersaturated or oversaturated, it is possible that the IQbot will be unable to detect structures, or may group structures together. Please ensure that the images you upload are saturated appropriately.
Clear of Artifacts:  Please take care to avoid creating bubbles, debris, or otherwise inducing artifacts into your image. Such additional features may corrupt the resulting data.
Clear of Accessary Information:  Please do not submit images retaining any annotations, legends, timestamps, or other markings. Every part of the images that you upload will be analyzed and included in the output data.
 

 

Generally, using software to analyze live cell images can present problems at the edges of the wells. At those edges, vignetting produced by the formation of fluid meniscus causes unreliable measurements under typical analysis conditions. However, to prevent those edge artifacts from potentially corrupting the output metrics, this IQbot incorporates targeting algorithms and flattening filters, counteracting any induced intensity gradient. Still, it is good practice to maintain consistent lighting throughout the region of interest in your submitted images.

 
 

If you are uncertain whether or not your images meet the quality standards for which this IQbot was validated, or have additional questions relating to image quality, please view our FAQs or contact the ImageQuantify.com support team.

 

     (216) 678-9258           help@imagequantify.com

 

Validation Information


This IQbot has been successfully validated for use, and a copy of its validation certificate accompanies the results of each order it analyzes. In accordance with general ImageQuantify.com validation procedures, this IQbot has been found to provide quantitative information equivalent to that of a manual observer, but with no variability and increased breadth of information.

 
 

The first step of the validation process was to establish the ground truth against which the IQbot’s efficacy would be compared. For the purpose of the Tube Formation Analysis IQbot, ground truth was defined on a manual observer basis. Trained observers were guided through a standardized, semi-automated process to produce output data from a specially selected validation image set. This image set represented the entire range of expected output values (e.g., images with scarce to incredibly dense systems of nodes and branches). Images were presented for analysis in a randomized order and deidentified to protect against observer biases. The observer mean for each parameter measured was accepted as ground truth.

By analyzing the inter- and intraobserver variability present in the manual observation process, acceptable criteria were established so as to compare the IQbot’s output to that of a trained replacement observer. Ultimately, the IQbot’s performance proved to offer significant upgrades over manual or semi-automated analysis techniques, and validation was considered successful. Further considerations ensured that the IQbot rejected and flagged concerning image-specific outputs and performed within operation time constraints.

 

Endothelial cells (EC), when provided with a 3D extracellular matrix substrate and appropriately supplemented growth media, will self-organize into a network of capillary-like "tubes." This in-vitro, "tube formation" assay, is commonly used to assess ability of compounds to stimulate or inhibit angiogenesis. To perform the assay, EC are usually seeded (subconfluently) in multi-well plates filled with matrigel (extracellular matrix). While not absolutely necessary (phase-contrast imaging is sufficient although often corrupted by uneven lighting due to the meniscus formed in each well), endothelial cell membranes may be labeled with fluorescent DiI dyes added directly to each well. Each well is subsequently imaged using a high resolution, wide-field microscope using both phase-contrast and fluorescence modes. Images are acquired while raster scanning across each well to form a single, high-resolution mosaic. Applying a series of custom morphological and spectral filters to the mosaic, the endothelial cell network can be segmented and skeletonized for nodal/branching analysis.

DEMO IMAGE
IQBOT APPLIED IMAGE

 

SAMPLE DATA
ImageVessel Area
(pixels)
Node Count3 Branch
Node Count
4 Branch
Node Count
5+ Branch
Node Count
Skeletal Length
(pixels)
Mean Node
Thickness
(pixels)
Standard Deviation
Node Thickness
(pixels)
Branch CountMean Branch
Thickness
(pixels)
Standard Deviation
Branch Thickness
Lumen CountMean Lumen
Area
(pixels)
Endpoint CountMean Node-to-Node
Length
(pixels)
Mean Endpoint-to-Node
Length
(pixels)
Sample 0.299806 185 138 39 8 17.243999 28.615253 12.993723 423 18.748137 10.985001 45 0.000413 259 40.25 33.388889