Difference between revisions of "20.109(S10):Protein-level analysis (Day6)"
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[[Image:20.109_CTE_analysis-ex.png|thumb|200px|'''Sample cell counting result for image above''']] | [[Image:20.109_CTE_analysis-ex.png|thumb|200px|'''Sample cell counting result for image above''']] | ||
− | Whatever the imaging modality, the resulting plethora of imaging information, especially if at the single-cell level or through multiple sections of a 3D-tissue, requires potent and efficient analysis tools. Many image analysis software packages are commercially available, with varying degrees of user-friendliness, algorithm efficiency, etc. Today, | + | Whatever the imaging modality, the resulting plethora of imaging information, especially if at the single-cell level or through multiple sections of a 3D-tissue, requires potent and efficient analysis tools. Many image analysis software packages are commercially available, with varying degrees of user-friendliness, algorithm efficiency, etc. Today, you ll use an open source analysis package from NIH called [http://rsb.info.nih.gov/ij/ ImageJ]. |
− | Basic image processing includes noise reduction, enhancement of brightness and contrast, thresholding images based on intensity (e.g., everything below a certain intensity value is considered background), and colorizing. For cells, typical analyses include measurement of surface area (i.e., how spread is the cell morphology?), tracking individual cell intensities (as you know from Module 1, these may reflect content of calcium or other molecules of interest), and counting cell populations. In general, analyses that require tracking cells over time are more complicated than static analyses. For example, tracking cell migration typically involves setting thresholds with respect to both intensity and size, then running an algorithm that calculates the centroid of each cell at each time-point and from those centroids the | + | Basic image processing includes noise reduction, enhancement of brightness and contrast, thresholding images based on intensity (e.g., everything below a certain intensity value is considered background), and colorizing. For cells, typical analyses include measurement of surface area (i.e., how spread is the cell morphology?), tracking individual cell intensities (as you know from Module 1, these may reflect content of calcium or other molecules of interest), and counting cell populations. In general, analyses that require tracking cells over time are more complicated than static analyses. For example, tracking cell migration typically involves setting thresholds with respect to both intensity and size, then running an algorithm that calculates the centroid of each cell at each time-point and from those centroids the cell s path and velocity. Fully automated tracking can be challenged by cells dropping in and out of the plane of view, crossing paths with other similar-looking cells, or just moving very quickly. On the other hand, fully manual tracking |