# Difference between revisions of "A Blind Spot in Confocal Reflection Microscopy: The Dependence of Fiber Brightness on Fiber Orientation in Imaging Biopolymer Networks"

(→Results) |
(→Results) |
||

Line 20: | Line 20: | ||

[[Image:jawerth1.jpg|400px|thumb|center|Fig. 1. Simultaneous imaging of a collagen network using CRM and CFM. A. Typical image from CRM. B. Corresponding image using CFM. Red circles highlight fibers that do not appear in the reflection image. C. Overlay of panels A (green) and B (red). D. Projection of 50 x,z slices along the y axis using CRM image data. E. Equivalent projection using CFM. F. Overlay of panels D (green) and E (red). ]] | [[Image:jawerth1.jpg|400px|thumb|center|Fig. 1. Simultaneous imaging of a collagen network using CRM and CFM. A. Typical image from CRM. B. Corresponding image using CFM. Red circles highlight fibers that do not appear in the reflection image. C. Overlay of panels A (green) and B (red). D. Projection of 50 x,z slices along the y axis using CRM image data. E. Equivalent projection using CFM. F. Overlay of panels D (green) and E (red). ]] | ||

− | To quantify the degree of alignment in the data sets, the authors use a grayscale moments analysis of the data to obtain a histogram of the orientations of the fibers in the sample. This yields values of the azimuthal angle <math>\phi</math> defined within the imaging plane and of the polar angle <math>\theta</math> defined with respect to the perpendicular (z) axis. Since the area of a surface element for a unit sphere is <math>sin \theta d\theta d\phi</math>, an isotropic network should show a sine distribution for <math>\theta</math> and a uniform distribution for <math>\phi</math>. | + | To quantify the degree of alignment in the data sets, the authors use a grayscale moments analysis of the data to obtain a histogram of the orientations of the fibers in the sample. This yields values of the azimuthal angle <math>\phi</math> defined within the imaging plane and of the polar angle <math>\theta</math> defined with respect to the perpendicular (z) axis. Since the area of a surface element for a unit sphere is <math>sin \theta d\theta d\phi</math>, an isotropic network should show a sine distribution for <math>\theta</math> and a uniform distribution for <math>\phi</math>. The distributions are shown in Figure 2. The <math>\phi</math> distribution is fairly flat for both imaging methods, revealing that the sample is isotropic within the focal plane. For <math>\theta</math>, CFM data follows a sine distribution whereas CRM data deviates strongly. |

+ | |||

+ | To test whether the anisotropy in the CRM data is an imaging artifact, the authors rotate the sample by 90 degrees. Whereas the CFM data shows no difference when rotated, the CRM anistropy does not rotate. This suggests that there is some bias within the CRM imaging technique itself. | ||

[[Image:jawerth2.gif|400px|thumb|center|Fig. 2. Relative frequency of the moment angle <math>\theta</math> for CFM data (triangles) and CRM data (circles) in both rotated (solid) and non-rotated (open) samples. Light-shaded line: sine distribution expected for an isotropic sample. Inset: corresponding <math>\phi</math> distributions.]] | [[Image:jawerth2.gif|400px|thumb|center|Fig. 2. Relative frequency of the moment angle <math>\theta</math> for CFM data (triangles) and CRM data (circles) in both rotated (solid) and non-rotated (open) samples. Light-shaded line: sine distribution expected for an isotropic sample. Inset: corresponding <math>\phi</math> distributions.]] | ||

+ | |||

+ | To determine the origin of the anisotropy, the authors calculate the median intensity for each fiber in the CFM and CRM data (Figure 3). In the CFM data, the intensity does not depend on the fiber angle, apart from a small increase in intensity for fibers that are perpendicular to the imaging plane. In contrast, for CRM, the intensity drops quite precipitously for fibers at more than 50 degrees from the imaging plane. As a result, for an isotropic 3-D network, CFM will detect almost twice as many fibers as CRM. | ||

[[Image:jawerth3.gif|400px|thumb|center|Fig. 3. Intensity of individual fibers as a function of their <math>\theta</math>-angle for CFM (triangles) and CRM (circles) for both the rotated (open) and nonrotated (solid) cases. Shaded line shows expected values from theory. ]] | [[Image:jawerth3.gif|400px|thumb|center|Fig. 3. Intensity of individual fibers as a function of their <math>\theta</math>-angle for CFM (triangles) and CRM (circles) for both the rotated (open) and nonrotated (solid) cases. Shaded line shows expected values from theory. ]] | ||

+ | |||

+ | The authors then do some modeling to explain why the intensity drops as a function of fiber angle in CRM. They show using simple geometry that for CFM, light that illuminates a fiber that forms an angle <math>\theta</math> with the z-axis will be reflected at an angle <math>2 \theta</math>. However the aperture of the imaging system has a maximal opening angle, and the amount of light it captures will depend on the angle of the reflected light. This approach predicts the steep dropoff in intensity for fibers with large angles with the imaging plane. | ||

== Discussion == | == Discussion == |

## Revision as of 18:58, 24 October 2010

Entry by Leon Furchtgott, APP 225 Fall 2010.

A Blind Spot in Confocal Reflection Microscopy: The Dependence of Fiber Brightness on Fiber Orientation in Imaging Biopolymer Networks. L.M. Jawerth, S. Munster, D.A. Vader, B. Fabry, and D.A. Weitz. (2010). Biophysical Journal, 98, L01-L03.

## Summary

The paper is interested in imaging techniques for biopolymers. In particular, the paper compares the effectiveness in imaging collagen networks of two confocal imaging methods, confocal reflection microscopy (CRM) and confocal fluorescence microscopy (CFM). The authors simultaneously image collagen using the two techniques and find that in CRM, fiber brightness depends strongly on fiber orientation. This explains why when using CRM to image collagen, the network appears to be aligned with the imaging plane, whereas in CFM, the network seems isotropic.

## Background

Difference between CRM and CFM: Both CRM and CFM are confocal techniques. CRM uses back-scattered light to form an image. CFM uses laser light to excite fluorophores in an imaging sample and forms an image from the emitted light.

Collagen : Collagen is a protein in mammals that forms the primary component of connective tissues in the interstitial space between cells. Collagen appears to be a branched network of fibers.

Previous research in collagen network architecture: Collagen 3-D architecture is the subject of a lot of research. To understand collagen's biological role, it is crucial to image the exact 3-D fiber environment in a cell. The most commonly used technique for imaging collage is CRM. CRM studies have shown anisotropy in collagen networks: the fibers tend to orient in the direction of the imaging plane. This might be an intrinsic property of collagen, or it could be caused by the imaging method. To determine the origin of the effect, the authors use CFM and CRM simultaneously on fluorescently labeled collagen type I networks.

## Results

Figure 1 shows a comparison of images taken with CRM and with CFM for the same collagen sample, and the differences in imaging are immediately visible. Whereas Figures 1B and 1E (CFM) show a collagen network with no preferential direction, Figures 1A and 1D (CRM) show a network whose fibers are oriented with the imaging plane. Figures 1B and 1E show many fibers that are aligned in the perpendicular direction from the imaging plane that do not show up in the CRM figures. All fibers detected by CRM are also detected by CFM, but a large number are detected only by CFM and not by CRM.

To quantify the degree of alignment in the data sets, the authors use a grayscale moments analysis of the data to obtain a histogram of the orientations of the fibers in the sample. This yields values of the azimuthal angle <math>\phi</math> defined within the imaging plane and of the polar angle <math>\theta</math> defined with respect to the perpendicular (z) axis. Since the area of a surface element for a unit sphere is <math>sin \theta d\theta d\phi</math>, an isotropic network should show a sine distribution for <math>\theta</math> and a uniform distribution for <math>\phi</math>. The distributions are shown in Figure 2. The <math>\phi</math> distribution is fairly flat for both imaging methods, revealing that the sample is isotropic within the focal plane. For <math>\theta</math>, CFM data follows a sine distribution whereas CRM data deviates strongly.

To test whether the anisotropy in the CRM data is an imaging artifact, the authors rotate the sample by 90 degrees. Whereas the CFM data shows no difference when rotated, the CRM anistropy does not rotate. This suggests that there is some bias within the CRM imaging technique itself.

To determine the origin of the anisotropy, the authors calculate the median intensity for each fiber in the CFM and CRM data (Figure 3). In the CFM data, the intensity does not depend on the fiber angle, apart from a small increase in intensity for fibers that are perpendicular to the imaging plane. In contrast, for CRM, the intensity drops quite precipitously for fibers at more than 50 degrees from the imaging plane. As a result, for an isotropic 3-D network, CFM will detect almost twice as many fibers as CRM.

The authors then do some modeling to explain why the intensity drops as a function of fiber angle in CRM. They show using simple geometry that for CFM, light that illuminates a fiber that forms an angle <math>\theta</math> with the z-axis will be reflected at an angle <math>2 \theta</math>. However the aperture of the imaging system has a maximal opening angle, and the amount of light it captures will depend on the angle of the reflected light. This approach predicts the steep dropoff in intensity for fibers with large angles with the imaging plane.

## Discussion

## Relation to Soft Matter

This paper gives insight into a more experimental area of soft-matter physics than what we covered in our discussions of polymers. In particular it shows the great sensitivity of results about biopolymers to the imaging technique used and the dangers in using the wrong imaging technique for looking at polymers.