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Graphical methods for quantifying macromolecules through bright field imaging.

Chang H, DeFilippis RA, Tlsty TD, Parvin B - Bioinformatics (2008)

Bottom Line: In rare cases where nuclear signal is significantly altered as a result of sample preparation, nuclear segmentation can be validated and corrected.Finally, segmented stained patterns are associated with each nuclear region following region-based tessellation.Compared to classical non-negative matrix factorization, proposed method: (i) improves color decomposition, (ii) has a better noise immunity, (iii) is more invariant to initial conditions and (iv) has a superior computing performance.

View Article: PubMed Central - PubMed

Affiliation: Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. hchang@lbl.gov

ABSTRACT
Bright field imaging of biological samples stained with antibodies and/or special stains provides a rapid protocol for visualizing various macromolecules. However, this method of sample staining and imaging is rarely employed for direct quantitative analysis due to variations in sample fixations, ambiguities introduced by color composition and the limited dynamic range of imaging instruments. We demonstrate that, through the decomposition of color signals, staining can be scored on a cell-by-cell basis. We have applied our method to fibroblasts grown from histologically normal breast tissue biopsies obtained from two distinct populations. Initially, nuclear regions are segmented through conversion of color images into gray scale, and detection of dark elliptic features. Subsequently, the strength of staining is quantified by a color decomposition model that is optimized by a graph cut algorithm. In rare cases where nuclear signal is significantly altered as a result of sample preparation, nuclear segmentation can be validated and corrected. Finally, segmented stained patterns are associated with each nuclear region following region-based tessellation. Compared to classical non-negative matrix factorization, proposed method: (i) improves color decomposition, (ii) has a better noise immunity, (iii) is more invariant to initial conditions and (iv) has a superior computing performance.

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Probability density functions corresponding to the fat content on a cell-by-cell basis for each of the two populations, where (a) corresponds to a population represented by Figure 8c and (b) corresponds to a population represented by Figure 8a. The KS test computes a P-value of 0.001 indicating that these two populations are different.
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Figure 7: Probability density functions corresponding to the fat content on a cell-by-cell basis for each of the two populations, where (a) corresponds to a population represented by Figure 8c and (b) corresponds to a population represented by Figure 8a. The KS test computes a P-value of 0.001 indicating that these two populations are different.

Mentions: We have applied our method to a dataset of 192 images of fibroblasts obtained from women from two distinct patient populations. These samples are imaged on a Nikon Eclipse TE 2000 E, which is equipped with a color camera with a spatial resolution of 1280 × 960 pixels and a dynamic range of 8 bits per channel in RGB space. The illumination power is maintained at the same level, and all images are automatically corrected for shading and non-uniformities against a blank slide. Images are processed, and the amount of lipid is quantified for each cell in each image. The net result of color decomposition is a binarized mask, shown in red in Figure 8, corresponding to positive stains, where the intensity in the red channel is aggregated on a cell-by-cell basis. In addition to color decomposition, Figure 8 indicates nuclear position, in green, and how the space between nuclear regions are partitioned through region-based tessellation, in yellow. Tessellation allows the signal complex to be associated with the corresponding nuclear region. The binarized masks provide the context for aggregating intensity features in the red channel, and associating them to each nucleus. Finally, the results are represented as two probability density functions for each population, as shown in Figure 7. The KS test between these two distributions computes a P-value of <0.0001, thus indicating that the two populations are different.Fig. 7.


Graphical methods for quantifying macromolecules through bright field imaging.

Chang H, DeFilippis RA, Tlsty TD, Parvin B - Bioinformatics (2008)

Probability density functions corresponding to the fat content on a cell-by-cell basis for each of the two populations, where (a) corresponds to a population represented by Figure 8c and (b) corresponds to a population represented by Figure 8a. The KS test computes a P-value of 0.001 indicating that these two populations are different.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666809&req=5

Figure 7: Probability density functions corresponding to the fat content on a cell-by-cell basis for each of the two populations, where (a) corresponds to a population represented by Figure 8c and (b) corresponds to a population represented by Figure 8a. The KS test computes a P-value of 0.001 indicating that these two populations are different.
Mentions: We have applied our method to a dataset of 192 images of fibroblasts obtained from women from two distinct patient populations. These samples are imaged on a Nikon Eclipse TE 2000 E, which is equipped with a color camera with a spatial resolution of 1280 × 960 pixels and a dynamic range of 8 bits per channel in RGB space. The illumination power is maintained at the same level, and all images are automatically corrected for shading and non-uniformities against a blank slide. Images are processed, and the amount of lipid is quantified for each cell in each image. The net result of color decomposition is a binarized mask, shown in red in Figure 8, corresponding to positive stains, where the intensity in the red channel is aggregated on a cell-by-cell basis. In addition to color decomposition, Figure 8 indicates nuclear position, in green, and how the space between nuclear regions are partitioned through region-based tessellation, in yellow. Tessellation allows the signal complex to be associated with the corresponding nuclear region. The binarized masks provide the context for aggregating intensity features in the red channel, and associating them to each nucleus. Finally, the results are represented as two probability density functions for each population, as shown in Figure 7. The KS test between these two distributions computes a P-value of <0.0001, thus indicating that the two populations are different.Fig. 7.

Bottom Line: In rare cases where nuclear signal is significantly altered as a result of sample preparation, nuclear segmentation can be validated and corrected.Finally, segmented stained patterns are associated with each nuclear region following region-based tessellation.Compared to classical non-negative matrix factorization, proposed method: (i) improves color decomposition, (ii) has a better noise immunity, (iii) is more invariant to initial conditions and (iv) has a superior computing performance.

View Article: PubMed Central - PubMed

Affiliation: Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. hchang@lbl.gov

ABSTRACT
Bright field imaging of biological samples stained with antibodies and/or special stains provides a rapid protocol for visualizing various macromolecules. However, this method of sample staining and imaging is rarely employed for direct quantitative analysis due to variations in sample fixations, ambiguities introduced by color composition and the limited dynamic range of imaging instruments. We demonstrate that, through the decomposition of color signals, staining can be scored on a cell-by-cell basis. We have applied our method to fibroblasts grown from histologically normal breast tissue biopsies obtained from two distinct populations. Initially, nuclear regions are segmented through conversion of color images into gray scale, and detection of dark elliptic features. Subsequently, the strength of staining is quantified by a color decomposition model that is optimized by a graph cut algorithm. In rare cases where nuclear signal is significantly altered as a result of sample preparation, nuclear segmentation can be validated and corrected. Finally, segmented stained patterns are associated with each nuclear region following region-based tessellation. Compared to classical non-negative matrix factorization, proposed method: (i) improves color decomposition, (ii) has a better noise immunity, (iii) is more invariant to initial conditions and (iv) has a superior computing performance.

Show MeSH