Systems and Methods of Analyzing Two Dimensional Gels

• US 20100046813A1
• Filed: 07/31/2007
• Published: 02/25/2010
• Est. Priority Date: 07/31/2006
• Status: Abandoned Application

• Current Assignee: Case Western Reserve University
• Original Assignee: Case Western Reserve University
• Inventors: Takamoto, Keiji, Chance, Mark
• Application Number:
  US12/376,051
• Publication Number:
  US 20100046813A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10064 Fluorescence image
• CPC Class Codes: G06T 2207/30072 Microarray; Biochip, DNA array; Well plate
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations

Abstract

Systems and methods of analyzing two dimensional gels are provided, in one embodiment, a method is provided for analyzing a 2-dimensiαnal gel. The method comprises receiving a first image of a gel based on a first protein sample labeled with a first fluorophore, receiving a second image of the gel based on a second protein sample labeled with a second fluorophore, applying linear normalization to image intensity values of the second image to provide a linear normalized image, and comparing image intensity values of the linear normalized image from image intensity values of the first image to provide a compared image.

Claims

1. A method for analyzing a 2-dimensional (2D) gel, the method comprising:

receiving a first image of a gel based on a first protein sample labeled with a first fluorophore;

receiving a second image of the gel based on a second protein sample labeled with a second fluorophore;

applying linear normalization to image intensity values of the second image based on the first image to provide a linear normalized image; and

comparing image intensity values of the linear normalized image with image intensity values of the first image to provide a compared image.

2. The method of claim 1, wherein the comparing image intensity values comprises performing a pixel by pixel Log ratio of image intensity values of the linear normalized image and image intensity values of the first image to provide a ratio image.

3. The method of claim 1, wherein the comparing image intensity values comprises performing a pixel by pixel subtraction of image intensity values of the linear normalized image and image intensity values of the first image to provide a differential image.

4. The method of claim 3, further comprising determining a second numerical derivative on image intensity values of the differential image to determine protein spot centers.

5. The method of claim 4, further comprising determining a third numerical derivative on image intensity values of the differential image to determine spot edges between overlapping protein spots.

6. The method of claim 4, further comprising performing a non-linear fitting on image intensity values of the differential image based on the determined protein spot centers to determine spot intensity volumes of protein spots on the differential image.

7. The method of claim 6, wherein the performing a nonlinear fitting on image intensity values of the differential image based on the determined protein spot centers comprises applying a skewed 2-D Gaussian parametric model on image intensity values of the differential image.

8. The method of claim 6, further comprising:

performing a second numerical derivative on image intensity values of one of tie first and second image to determine protein spot centers;

performing a nonlinear fitting on image intensity values of the one of the first and second image based on the determined protein spot centers to determine spot intensity volumes of protein spots on the one of the first and second image; and

determining spot intensity volume changes based on comparing the spot intensity volumes of the one of the first and second image and the differential image.

9. The method of claim 8, further performing a spot matching to match spots on the one of the first and second image with spots on the differential image and performing statistical analysis on the matched spots.

10. The method of claim 3, further comprising determining a second numerical derivative of the differential image and multiplying the differential image by the second numerical derivative to provide a modified differential image.

11. The method of claim 10, further comprising analyzing the modified differential image to determine initial parameter for performing a non-linear fitting on image intensity values of the modified differential image to determine spot intensity volumes of protein spots on the differential image.

12. The method of claim 11, wherein the initial parameters comprise spot centers, spot widths and spot amplitudes.

13. The method of claim 11, wherein the performing a non-linear fitting on image intensity values of the differential image based on the determined protein spot centers comprises applying a skewed 2-D Gaussian parametric model on image intensity values of the modified differential image employing the determined initial parameters.

14. The method of claim 1, wherein applying linear normalization to image intensity values of the second image to provide a linear normalized image comprises;

performing linear interpolation to determine coefficients of a linear equation; and

replacing intensity values of the second image with intensity values based on the linear equation.

15. The method of claim 14, wherein the performing linear interpolation and replacing intensity values is applied independently to different regions on the second image.

16. A computer readable medium having computer executable instructions for performing the method comprising:

receiving a first image of a 2-D differential gel based on a first protein sample labeled with a first fluorophore;

receiving a second image of the 2-D differential gel based on a second protein sample labeled with a second fluorophore;

applying linear normalization to image intensity values of the second image based on the first Image to provide a linear normalized image;

performing a pixel by pixel subtraction of image intensity values of the linear normalized image and image intensity values of the first image to provide a differential image;

determining a second numerical derivative on image intensity values of the differential image to determine protein spot centers; and

performing a non-linear fitting on image intensity values of the differential image based on the determined protein spot centers to determine spot intensity volumes of protein spots on the differential image.

17. The computer readable medium of claim 16, further comprising determining a third numerical derivative on image intensity values of the differential image to determine spot edges between overlapping protein spots.

18. The computer readable medium of claim 16, wherein the performing a non-linear fitting on image intensity values of the differential image based on the determined protein spot centers comprises applying a skewed 2-D Gaussian parametric model on image intensity values of the differential image.

19. The computer readable medium of claim 16, further comprising:

performing a second numerical derivative on image intensity values of one of the first and second image to determine protein spot centers;

performing a non-linear fitting on image intensity values of the one of the first and second image based on the determined protein spot centers to determine spot intensity volumes of protein spots on the one of the first and second image; and

determining spot intensity volume changes based on comparing the spot intensity volumes of the one of the first and second image and the differential image.

20. The computer readable medium of claim 19, further performing a spot matching to match spots on the one of the first and second image with spots on the differential image and performing statistical analysis on the matched spots.

21. The computer readable medium of claim 16, further comprising:

multiplying the differential image by the second numerical derivative to provide a modified differential image;

analyzing the modified differential image to determine initial parameters comprising at least one of spot centers, spot amplitudes and spot widths; and

performing a skewed 2-D Gaussian parametric model on image intensity vales of the modified differential image employing the determined initial parameters to determine spot volumes.

22. The computer readable medium of claim 16, wherein applying linear normalization to image intensity values of the second image to provide a linear normalized image comprises;

performing linear interpolation to determine coefficients of a linear equation; and

replacing intensity values of the second image with intensity values based on the linear equation, wherein the performing linear interpolation and replacing intensity values is applied independently to different regions on the second image.

23. A system for analyzing a 2-dimensional (2D) gel, the system comprising:

an image normalization and compare module that applies linear normalization to one of a first image of a gel based on a first protein sample labeled with a first fluorophore and a second image of the gel based on a second protein sample labeled with a second fluorophore based on the other of the first and second image and generates a compared image that is a comparison of a normalized one of the first image and second image to a non-normalized one of the first image and second image; and

a spot detection and fitting component that performs a non-linear fitting on image intensity values of the compared image based on determined protein spot centers to determine spot intensity volumes of protein spots on the compared image.

24. The system of claim 23, wherein the compared image is generated based on a pixel by pixel logarithmic ratio of image intensity values of the normalized one of the first image and second image and image intensity values of the non-normalized one of the first image and second image to provide a ratio image.

25. The system of claim 23, wherein the compared image is generated based on a pixel by pixel subtraction of image intensity values of the normalized one of the first image and second image and image intensity values of the non-normalized one of the first image and second image to provide a differential image.

26. The system of claim 23, wherein the non-linear fitting is a skewed 2-D Gaussian parametric model.

27. The system of claim 23, the spot detection and fitting component further determines a second numerical derivative on image intensity values of the compared image to determine initial parameter for the non-linear fitting, the initial parameters comprising protein spot centers, spot amplitudes and spot widths.

28. The system of claim 27, the spot detection and fitting component further determines a third numerical derivative on image intensity values of the compared image to determine spot edges between overlapping protein spots

29. The system of claim 23, the image normalization and compare module applies linear normalization independently to different regions on the normalized image.