However I am not fully satisfied with this approach. the nuclei of the cells which lack expression are not counted and most of the nuclei of the cells which have expression are counted. Comparison of the outcomes of such analysis with examination “by eye” reveals that it quite OK i.e. I am aware that this is biased by the fact that all cells in which the expression is located a bit farther from nucleus will be counted as negative. The increase or decrease in image gradient leads to the corresponding rise. The NIH has published a introductory chapter of. HC aggregation measure HC/EC ratio based on pixel differences estimates the fraction of the HC aggregates in the nucleus. The data can be downloaded from the Image Data Resource. Our introduction to automated image analysis principles and practicalities is published as an educational article at PLoS. Membrane-less organelle in the nucleus of the cell Functions: ribosome biogenesis and cell cycle regulation.image-55 Speaker Notes In the DNA channel, the nucleoli is shown as the absence of DNA (red arrows).
Cellprofiler count aggregates around nucleus software#
So I simply pick nuclei which have at least some contact with my protein of interest and consider number of such nuclei as a count of the positive cells. Technical descriptions of CellProfiler and CellProfiler Analyst software can be found in our papers while more written tutorials can be found on the CellProfiler GitHub page. I set a binary picture of nuclei channel as “Object image” and a binary picture of my protein channel as “Selector image” and “Object overlap” feature I set at 1%. What I am using now is Binary Feature Extractor function in BioVoxxel plugin. Thus, some another solution must be applied. Unfortunately this “supporting” staining was not supportive enough i.e. Of course I wouldn’t like to draw the cells’ borders manually in hundreds of pictures and I hoped that some edge detection tool would do it for me. My first idea was that I could use some edge detection tool to obtain a map of the cells’ borders based on this staining and then somehow overlap it with a map of my protein of interest expression and use this to count positive and negative cells. There is also staining of yet another protein which locates in cell membrane which I hoped would be useful to delineate cells’ borders for the analysis. I have wide-field images of immunostained cells with nuclei stained with DAPI and my protein of interest stained with another fluorophore. common errors are counting two nearby nuclei as one nucleus with twice the. Expression of the protein is scattered all over the cell with no regular pattern with some tendency to be in a proximity of nucleus. CellProfiler can address a variety of biological questions quantitatively. Maybe someone more familiar with image analysis can help me with this. I am using ImageJ to solve this problem but I believe that there is better solution than the one that I came up with. Of course I would like the computer to do it for me.
I would like to quantify the fraction of both depending on certain culture conditions. Some of them do express it, some others don’t. My protein of interest is not equally expressed among the cells in culture.
0 kommentar(er)
